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PaddleOCR

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PaddleOCR
未验证 提交 752a277a 编写于 作者: M MissPenguin 提交者： GitHub
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Merge branch 'dygraph' into dygraph

上级 8ae07313 2acde6c5
隐藏空白更改
内联 并排
Showing with 689 addition and 109 deletion
PPOCRLabel/PPOCRLabel.py
浏览文件 @ 752a277a
......@@ -21,12 +21,13 @@ import os.path
import platform
import subprocess
import sys
import xlrd
from functools import partial
from PyQt5.QtCore import QSize, Qt, QPoint, QByteArray, QTimer, QFileInfo, QPointF, QProcess
from PyQt5.QtGui import QImage, QCursor, QPixmap, QImageReader
from PyQt5.QtWidgets import QMainWindow, QListWidget, QVBoxLayout, QToolButton, QHBoxLayout, QDockWidget, QWidget, \
QSlider, QGraphicsOpacityEffect, QMessageBox, QListView, QScrollArea, QWidgetAction, QApplication, QLabel, \
QSlider, QGraphicsOpacityEffect, QMessageBox, QListView, QScrollArea, QWidgetAction, QApplication, QLabel, QGridLayout, \
QFileDialog, QListWidgetItem, QComboBox, QDialog
__dir__ = os.path.dirname(os.path.abspath(__file__))
......@@ -36,7 +37,7 @@ sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../PaddleOCR')))
sys.path.append("..")
from paddleocr import PaddleOCR
from paddleocr import PaddleOCR, PPStructure
from libs.constants import *
from libs.utils import *
from libs.labelColor import label_colormap
......@@ -100,9 +101,15 @@ class MainWindow(QMainWindow):
use_gpu=gpu,
lang=lang,
show_log=False)
self.table_ocr = PPStructure(use_pdserving=False,
use_gpu=gpu,
lang=lang,
layout=False,
show_log=False)
if os.path.exists('./data/paddle.png'):
result = self.ocr.ocr('./data/paddle.png', cls=True, det=True)
result = self.table_ocr('./data/paddle.png', return_ocr_result_in_table=True)
# For loading all image under a directory
self.mImgList = []
......@@ -196,16 +203,25 @@ class MainWindow(QMainWindow):
self.reRecogButton.setIcon(newIcon('reRec', 30))
self.reRecogButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon)
self.tableRecButton = QToolButton()
self.tableRecButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon)
self.newButton = QToolButton()
self.newButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon)
self.createpolyButton = QToolButton()
self.createpolyButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon)
self.SaveButton = QToolButton()
self.SaveButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon)
self.DelButton = QToolButton()
self.DelButton.setToolButtonStyle(Qt.ToolButtonTextBesideIcon)
leftTopToolBox = QHBoxLayout()
leftTopToolBox.addWidget(self.newButton)
leftTopToolBox.addWidget(self.reRecogButton)
leftTopToolBox = QGridLayout()
leftTopToolBox.addWidget(self.newButton, 0, 0, 1, 1)
leftTopToolBox.addWidget(self.createpolyButton, 0, 1, 1, 1)
leftTopToolBox.addWidget(self.reRecogButton, 1, 0, 1, 1)
leftTopToolBox.addWidget(self.tableRecButton, 1, 1, 1, 1)
leftTopToolBoxContainer = QWidget()
leftTopToolBoxContainer.setLayout(leftTopToolBox)
listLayout.addWidget(leftTopToolBoxContainer)
......@@ -446,13 +462,22 @@ class MainWindow(QMainWindow):
'Ctrl+R', 'reRec', getStr('singleRe'), enabled=False)
createpoly = action(getStr('creatPolygon'), self.createPolygon,
'q', 'new', getStr('creatPolygon'), enabled=True)
'q', 'new', getStr('creatPolygon'), enabled=False)
tableRec = action(getStr('TableRecognition'), self.TableRecognition,
'', 'Auto', getStr('TableRecognition'), enabled=False)
cellreRec = action(getStr('cellreRecognition'), self.cellreRecognition,
'', 'reRec', getStr('cellreRecognition'), enabled=False)
saveRec = action(getStr('saveRec'), self.saveRecResult,
'', 'save', getStr('saveRec'), enabled=False)
saveLabel = action(getStr('saveLabel'), self.saveLabelFile, #
'Ctrl+S', 'save', getStr('saveLabel'), enabled=False)
exportJSON = action(getStr('exportJSON'), self.exportJSON,
'', 'save', getStr('exportJSON'), enabled=False)
undoLastPoint = action(getStr("undoLastPoint"), self.canvas.undoLastPoint,
'Ctrl+Z', "undo", getStr("undoLastPoint"), enabled=False)
......@@ -474,10 +499,12 @@ class MainWindow(QMainWindow):
self.editButton.setDefaultAction(edit)
self.newButton.setDefaultAction(create)
self.createpolyButton.setDefaultAction(createpoly)
self.DelButton.setDefaultAction(deleteImg)
self.SaveButton.setDefaultAction(save)
self.AutoRecognition.setDefaultAction(AutoRec)
self.reRecogButton.setDefaultAction(reRec)
self.tableRecButton.setDefaultAction(tableRec)
# self.preButton.setDefaultAction(openPrevImg)
# self.nextButton.setDefaultAction(openNextImg)
......@@ -523,25 +550,25 @@ class MainWindow(QMainWindow):
# Store actions for further handling.
self.actions = struct(save=save, resetAll=resetAll, deleteImg=deleteImg,
lineColor=color1, create=create, delete=delete, edit=edit, copy=copy,
saveRec=saveRec, singleRere=singleRere, AutoRec=AutoRec, reRec=reRec,
lineColor=color1, create=create, createpoly=createpoly, tableRec=tableRec, delete=delete, edit=edit, copy=copy,
saveRec=saveRec, singleRere=singleRere, AutoRec=AutoRec, reRec=reRec, cellreRec=cellreRec,
createMode=createMode, editMode=editMode,
shapeLineColor=shapeLineColor, shapeFillColor=shapeFillColor,
zoom=zoom, zoomIn=zoomIn, zoomOut=zoomOut, zoomOrg=zoomOrg,
fitWindow=fitWindow, fitWidth=fitWidth,
zoomActions=zoomActions, saveLabel=saveLabel, change_cls=change_cls,
undo=undo, undoLastPoint=undoLastPoint, open_dataset_dir=open_dataset_dir,
rotateLeft=rotateLeft, rotateRight=rotateRight, lock=lock,
fileMenuActions=(opendir, open_dataset_dir, saveLabel, resetAll, quit),
rotateLeft=rotateLeft, rotateRight=rotateRight, lock=lock, exportJSON=exportJSON,
fileMenuActions=(opendir, open_dataset_dir, saveLabel, exportJSON, resetAll, quit),
beginner=(), advanced=(),
editMenu=(createpoly, edit, copy, delete, singleRere, None, undo, undoLastPoint,
editMenu=(createpoly, edit, copy, delete, singleRere, cellreRec, None, undo, undoLastPoint,
None, rotateLeft, rotateRight, None, color1, self.drawSquaresOption, lock,
None, change_cls),
beginnerContext=(
create, edit, copy, delete, singleRere, rotateLeft, rotateRight, lock, change_cls),
create, createpoly, edit, copy, delete, singleRere, cellreRec, rotateLeft, rotateRight, lock, change_cls),
advancedContext=(createMode, editMode, edit, copy,
delete, shapeLineColor, shapeFillColor),
onLoadActive=(create, createMode, editMode),
onLoadActive=(create, createpoly, createMode, editMode),
onShapesPresent=(hideAll, showAll))
# menus
......@@ -574,7 +601,7 @@ class MainWindow(QMainWindow):
self.autoSaveOption.triggered.connect(self.autoSaveFunc)
addActions(self.menus.file,
(opendir, open_dataset_dir, None, saveLabel, saveRec, self.autoSaveOption, None, resetAll, deleteImg,
(opendir, open_dataset_dir, None, saveLabel, saveRec, exportJSON, self.autoSaveOption, None, resetAll, deleteImg,
quit))
addActions(self.menus.help, (showKeys, showSteps, showInfo))
......@@ -585,7 +612,7 @@ class MainWindow(QMainWindow):
zoomIn, zoomOut, zoomOrg, None,
fitWindow, fitWidth))
addActions(self.menus.autolabel, (AutoRec, reRec, alcm, None, help))
addActions(self.menus.autolabel, (AutoRec, reRec, cellreRec, alcm, None, help))
self.menus.file.aboutToShow.connect(self.updateFileMenu)
......@@ -695,6 +722,7 @@ class MainWindow(QMainWindow):
self.dirty = False
self.actions.save.setEnabled(False)
self.actions.create.setEnabled(True)
self.actions.createpoly.setEnabled(True)
def toggleActions(self, value=True):
"""Enable/Disable widgets which depend on an opened image."""
......@@ -780,6 +808,7 @@ class MainWindow(QMainWindow):
assert self.beginner()
self.canvas.setEditing(False)
self.actions.create.setEnabled(False)
self.actions.createpoly.setEnabled(False)
self.canvas.fourpoint = False
def createPolygon(self):
......@@ -787,10 +816,10 @@ class MainWindow(QMainWindow):
self.canvas.setEditing(False)
self.canvas.fourpoint = True
self.actions.create.setEnabled(False)
self.actions.createpoly.setEnabled(False)
self.actions.undoLastPoint.setEnabled(True)
def rotateImg(self, filename, k, _value):
self.actions.rotateRight.setEnabled(_value)
pix = cv2.imread(filename)
pix = np.rot90(pix, k)
......@@ -831,6 +860,7 @@ class MainWindow(QMainWindow):
self.canvas.setEditing(True)
self.canvas.restoreCursor()
self.actions.create.setEnabled(True)
self.actions.createpoly.setEnabled(True)
def toggleDrawMode(self, edit=True):
self.canvas.setEditing(edit)
......@@ -987,11 +1017,21 @@ class MainWindow(QMainWindow):
if len(self.canvas.selectedShapes) == 1 and self.keyList.count() > 0:
selected_key_item_row = self.keyList.findItemsByLabel(self.canvas.selectedShapes[0].key_cls,
get_row=True)
if isinstance(selected_key_item_row, list) and len(selected_key_item_row) == 0:
key_text = self.canvas.selectedShapes[0].key_cls
item = self.keyList.createItemFromLabel(key_text)
self.keyList.addItem(item)
rgb = self._get_rgb_by_label(key_text, self.kie_mode)
self.keyList.setItemLabel(item, key_text, rgb)
selected_key_item_row = self.keyList.findItemsByLabel(self.canvas.selectedShapes[0].key_cls,
get_row=True)
self.keyList.setCurrentRow(selected_key_item_row)
self._noSelectionSlot = False
n_selected = len(selected_shapes)
self.actions.singleRere.setEnabled(n_selected)
self.actions.cellreRec.setEnabled(n_selected)
self.actions.delete.setEnabled(n_selected)
self.actions.copy.setEnabled(n_selected)
self.actions.edit.setEnabled(n_selected == 1)
......@@ -1216,6 +1256,7 @@ class MainWindow(QMainWindow):
if self.beginner(): # Switch to edit mode.
self.canvas.setEditing(True)
self.actions.create.setEnabled(True)
self.actions.createpoly.setEnabled(True)
self.actions.undoLastPoint.setEnabled(False)
self.actions.undo.setEnabled(True)
else:
......@@ -1654,8 +1695,10 @@ class MainWindow(QMainWindow):
self.haveAutoReced = False
self.AutoRecognition.setEnabled(True)
self.reRecogButton.setEnabled(True)
self.tableRecButton.setEnabled(True)
self.actions.AutoRec.setEnabled(True)
self.actions.reRec.setEnabled(True)
self.actions.tableRec.setEnabled(True)
self.actions.open_dataset_dir.setEnabled(True)
self.actions.rotateLeft.setEnabled(True)
self.actions.rotateRight.setEnabled(True)
......@@ -1755,6 +1798,7 @@ class MainWindow(QMainWindow):
self.openNextImg()
self.actions.saveRec.setEnabled(True)
self.actions.saveLabel.setEnabled(True)
self.actions.exportJSON.setEnabled(True)
elif mode == 'Auto':
if annotationFilePath and self.saveLabels(annotationFilePath, mode=mode):
......@@ -2081,6 +2125,280 @@ class MainWindow(QMainWindow):
self.singleLabel(shape)
self.setDirty()
def TableRecognition(self):
'''
Table Recegnition
'''
from paddleocr.ppstructure.table.predict_table import to_excel
import time
start = time.time()
img = cv2.imread(self.filePath)
res = self.table_ocr(img, return_ocr_result_in_table=True)
TableRec_excel_dir = self.lastOpenDir + '/tableRec_excel_output/'
os.makedirs(TableRec_excel_dir, exist_ok=True)
filename, _ = os.path.splitext(os.path.basename(self.filePath))
excel_path = TableRec_excel_dir + '{}.xlsx'.format(filename)
if res is None:
msg = 'Can not recognise the table in ' + self.filePath + '. Please change manually'
QMessageBox.information(self, "Information", msg)
to_excel('', excel_path) # create an empty excel
return
# save res
# ONLY SUPPORT ONE TABLE in one image
hasTable = False
for region in res:
if region['type'] == 'Table':
if region['res']['boxes'] is None:
msg = 'Can not recognise the detection box in ' + self.filePath + '. Please change manually'
QMessageBox.information(self, "Information", msg)
to_excel('', excel_path) # create an empty excel
return
hasTable = True
# save table ocr result on PPOCRLabel
# clear all old annotaions before saving result
self.itemsToShapes.clear()
self.shapesToItems.clear()
self.itemsToShapesbox.clear() # ADD
self.shapesToItemsbox.clear()
self.labelList.clear()
self.BoxList.clear()
self.result_dic = []
self.result_dic_locked = []
shapes = []
result_len = len(region['res']['boxes'])
for i in range(result_len):
bbox = np.array(region['res']['boxes'][i])
rec_text = region['res']['rec_res'][i][0]
# polys to rectangles
x1, y1 = np.min(bbox[:, 0]), np.min(bbox[:, 1])
x2, y2 = np.max(bbox[:, 0]), np.max(bbox[:, 1])
rext_bbox = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]]
# save bbox to shape
shape = Shape(label=rec_text, line_color=DEFAULT_LINE_COLOR, key_cls=None)
for point in rext_bbox:
x, y = point
# Ensure the labels are within the bounds of the image.
# If not, fix them.
x, y, snapped = self.canvas.snapPointToCanvas(x, y)
shape.addPoint(QPointF(x, y))
shape.difficult = False
# shape.locked = False
shape.close()
self.addLabel(shape)
shapes.append(shape)
self.setDirty()
self.canvas.loadShapes(shapes)
# save HTML result to excel
try:
to_excel(region['res']['html'], excel_path)
except:
print('Can not save excel file, maybe Permission denied (.xlsx is being occupied)')
break
if not hasTable:
msg = 'Can not recognise the table in ' + self.filePath + '. Please change manually'
QMessageBox.information(self, "Information", msg)
to_excel('', excel_path) # create an empty excel
return
# automatically open excel annotation file
if platform.system() == 'Windows':
try:
import win32com.client
except:
print("CANNOT OPEN .xlsx. It could be one of the following reasons: " \
"Only support Windows | No python win32com")
try:
xl = win32com.client.Dispatch("Excel.Application")
xl.Visible = True
xl.Workbooks.Open(excel_path)
# excelEx = "You need to show the excel executable at this point"
# subprocess.Popen([excelEx, excel_path])
# os.startfile(excel_path)
except:
print("CANNOT OPEN .xlsx. It could be the following reasons: " \
".xlsx is not existed")
else:
os.system('open ' + os.path.normpath(excel_path))
print('time cost: ', time.time() - start)
def cellreRecognition(self):
'''
re-recognise text in a cell
'''
img = cv2.imread(self.filePath)
for shape in self.canvas.selectedShapes:
box = [[int(p.x()), int(p.y())] for p in shape.points]
if len(box) > 4:
box = self.gen_quad_from_poly(np.array(box))
assert len(box) == 4
# pad around bbox for better text recognition accuracy
_box = boxPad(box, img.shape, 6)
img_crop = get_rotate_crop_image(img, np.array(_box, np.float32))
if img_crop is None:
msg = 'Can not recognise the detection box in ' + self.filePath + '. Please change manually'
QMessageBox.information(self, "Information", msg)
return
# merge the text result in the cell
texts = ''
probs = 0. # the probability of the cell is avgerage prob of every text box in the cell
bboxes = self.ocr.ocr(img_crop, det=True, rec=False, cls=False)
if len(bboxes) > 0:
bboxes.reverse() # top row text at first
for _bbox in bboxes:
patch = get_rotate_crop_image(img_crop, np.array(_bbox, np.float32))
rec_res = self.ocr.ocr(patch, det=False, rec=True, cls=False)
text = rec_res[0][0]
if text != '':
texts += text + (' ' if text[0].isalpha() else '') # add space between english word
probs += rec_res[0][1]
probs = probs / len(bboxes)
result = [(texts.strip(), probs)]
if result[0][0] != '':
result.insert(0, box)
print('result in reRec is ', result)
if result[1][0] == shape.label:
print('label no change')
else:
shape.label = result[1][0]
else:
print('Can not recognise the box')
if self.noLabelText == shape.label:
print('label no change')
else:
shape.label = self.noLabelText
self.singleLabel(shape)
self.setDirty()
def exportJSON(self):
'''
export PPLabel and CSV to JSON (PubTabNet)
'''
import pandas as pd
from libs.dataPartitionDialog import DataPartitionDialog
# data partition user input
partitionDialog = DataPartitionDialog(parent=self)
partitionDialog.exec()
if partitionDialog.getStatus() == False:
return
# automatically save annotations
self.saveFilestate()
self.savePPlabel(mode='auto')
# load box annotations
labeldict = {}
if not os.path.exists(self.PPlabelpath):
msg = 'ERROR, Can not find Label.txt'
QMessageBox.information(self, "Information", msg)
return
else:
with open(self.PPlabelpath, 'r', encoding='utf-8') as f:
data = f.readlines()
for each in data:
file, label = each.split('\t')
if label:
label = label.replace('false', 'False')
label = label.replace('true', 'True')
labeldict[file] = eval(label)
else:
labeldict[file] = []
# if len(labeldict) != len(csv_paths):
# msg = 'ERROR, box label and excel label are not in the same number\n' + \
# 'box label: ' + str(len(labeldict)) + '\n' + \
# 'excel label: ' + str(len(csv_paths)) + '\n' + \
# 'Please check the label.txt and tableRec_excel_output\n'
# QMessageBox.information(self, "Information", msg)
# return
train_split, val_split, test_split = partitionDialog.getDataPartition()
# check validate
if train_split + val_split + test_split > 100:
msg = "The sum of training, validation and testing data should be less than 100%"
QMessageBox.information(self, "Information", msg)
return
print(train_split, val_split, test_split)
train_split, val_split, test_split = float(train_split) / 100., float(val_split) / 100., float(test_split) / 100.
train_id = int(len(labeldict) * train_split)
val_id = int(len(labeldict) * (train_split + val_split))
print('Data partition: train:', train_id,
'validation:', val_id - train_id,
'test:', len(labeldict) - val_id)
TableRec_excel_dir = os.path.join(self.lastOpenDir, 'tableRec_excel_output')
json_results = []
imgid = 0
for image_path in labeldict.keys():
# load csv annotations
filename, _ = os.path.splitext(os.path.basename(image_path))
csv_path = os.path.join(TableRec_excel_dir, filename + '.xlsx')
if not os.path.exists(csv_path):
msg = 'ERROR, Can not find ' + csv_path
QMessageBox.information(self, "Information", msg)
return
# read xlsx file, convert to HTML
# xd = pd.ExcelFile(csv_path)
# df = xd.parse()
# structure = df.to_html(index = False)
excel = xlrd.open_workbook(csv_path)
sheet0 = excel.sheet_by_index(0) # only sheet 0
merged_cells = sheet0.merged_cells # (0,1,1,3) start row, end row, start col, end col
html_list = [['td'] * sheet0.ncols for i in range(sheet0.nrows)]
for merged in merged_cells:
html_list = expand_list(merged, html_list)
token_list = convert_token(html_list)
# load box annotations
cells = []
for anno in labeldict[image_path]:
tokens = list(anno['transcription'])
obb = anno['points']
hbb = OBB2HBB(np.array(obb)).tolist()
cells.append({'tokens': tokens, 'bbox': hbb})
# data split
if imgid < train_id:
split = 'train'
elif imgid < val_id:
split = 'val'
else:
split = 'test'
# save dict
html = {'structure': {'tokens': token_list}, 'cell': cells}
json_results.append({'filename': os.path.basename(image_path), 'split': split, 'imgid': imgid, 'html': html})
imgid += 1
# save json
with open("{}/annotation.json".format(self.lastOpenDir), "w", encoding='utf-8') as fid:
fid.write(json.dumps(json_results, ensure_ascii=False))
msg = 'JSON sucessfully saved in {}/annotation.json'.format(self.lastOpenDir)
QMessageBox.information(self, "Information", msg)
def autolcm(self):
vbox = QVBoxLayout()
hbox = QHBoxLayout()
......@@ -2120,6 +2438,12 @@ class MainWindow(QMainWindow):
del self.ocr
self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=False,
lang=lg_idx[self.comboBox.currentText()])
del self.table_ocr
self.table_ocr = PPStructure(use_pdserving=False,
use_gpu=False,
lang=lg_idx[self.comboBox.currentText()],
layout=False,
show_log=False)
self.dialog.close()
def cancel(self):
......@@ -2138,6 +2462,7 @@ class MainWindow(QMainWindow):
self.fileStatedict[file] = 1
self.actions.saveLabel.setEnabled(True)
self.actions.saveRec.setEnabled(True)
self.actions.exportJSON.setEnabled(True)
def saveFilestate(self):
with open(self.fileStatepath, 'w', encoding='utf-8') as f:
......
PPOCRLabel/libs/dataPartitionDialog.py 0 → 100644
浏览文件 @ 752a277a
try:
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
except ImportError:
from PyQt4.QtGui import *
from PyQt4.QtCore import *
from libs.utils import newIcon
import time
import datetime
import json
import cv2
import numpy as np
BB = QDialogButtonBox
class DataPartitionDialog(QDialog):
def __init__(self, parent=None):
super().__init__()
self.parnet = parent
self.title = 'DATA PARTITION'
self.train_ratio = 70
self.val_ratio = 15
self.test_ratio = 15
self.initUI()
def initUI(self):
self.setWindowTitle(self.title)
self.setWindowModality(Qt.ApplicationModal)
self.flag_accept = True
if self.parnet.lang == 'ch':
msg = "导出JSON前请保存所有图像的标注且关闭EXCEL!"
else:
msg = "Please save all the annotations and close the EXCEL before exporting JSON!"
info_msg = QLabel(msg, self)
info_msg.setWordWrap(True)
info_msg.setStyleSheet("color: red")
info_msg.setFont(QFont('Arial', 12))
train_lbl = QLabel('Train split: ', self)
train_lbl.setFont(QFont('Arial', 15))
val_lbl = QLabel('Valid split: ', self)
val_lbl.setFont(QFont('Arial', 15))
test_lbl = QLabel('Test split: ', self)
test_lbl.setFont(QFont('Arial', 15))
self.train_input = QLineEdit(self)
self.train_input.setFont(QFont('Arial', 15))
self.val_input = QLineEdit(self)
self.val_input.setFont(QFont('Arial', 15))
self.test_input = QLineEdit(self)
self.test_input.setFont(QFont('Arial', 15))
self.train_input.setText(str(self.train_ratio))
self.val_input.setText(str(self.val_ratio))
self.test_input.setText(str(self.test_ratio))
validator = QIntValidator(0, 100)
self.train_input.setValidator(validator)
self.val_input.setValidator(validator)
self.test_input.setValidator(validator)
gridlayout = QGridLayout()
gridlayout.addWidget(info_msg, 0, 0, 1, 2)
gridlayout.addWidget(train_lbl, 1, 0)
gridlayout.addWidget(val_lbl, 2, 0)
gridlayout.addWidget(test_lbl, 3, 0)
gridlayout.addWidget(self.train_input, 1, 1)
gridlayout.addWidget(self.val_input, 2, 1)
gridlayout.addWidget(self.test_input, 3, 1)
bb = BB(BB.Ok | BB.Cancel, Qt.Horizontal, self)
bb.button(BB.Ok).setIcon(newIcon('done'))
bb.button(BB.Cancel).setIcon(newIcon('undo'))
bb.accepted.connect(self.validate)
bb.rejected.connect(self.cancel)
gridlayout.addWidget(bb, 4, 0, 1, 2)
self.setLayout(gridlayout)
self.show()
def validate(self):
self.flag_accept = True
self.accept()
def cancel(self):
self.flag_accept = False
self.reject()
def getStatus(self):
return self.flag_accept
def getDataPartition(self):
self.train_ratio = int(self.train_input.text())
self.val_ratio = int(self.val_input.text())
self.test_ratio = int(self.test_input.text())
return self.train_ratio, self.val_ratio, self.test_ratio
def closeEvent(self, event):
self.flag_accept = False
self.reject()
PPOCRLabel/libs/utils.py
浏览文件 @ 752a277a
......@@ -161,6 +161,77 @@ def get_rotate_crop_image(img, points):
print(e)
def boxPad(box, imgShape, pad : int) -> np.array:
"""
Pad a box with [pad] pixels on each side.
"""
box = np.array(box, dtype=np.int32)
box[0][0], box[0][1] = box[0][0] - pad, box[0][1] - pad
box[1][0], box[1][1] = box[1][0] + pad, box[1][1] - pad
box[2][0], box[2][1] = box[2][0] + pad, box[2][1] + pad
box[3][0], box[3][1] = box[3][0] - pad, box[3][1] + pad
h, w, _ = imgShape
box[:,0] = np.clip(box[:,0], 0, w)
box[:,1] = np.clip(box[:,1], 0, h)
return box
def OBB2HBB(obb) -> np.array:
"""
Convert Oriented Bounding Box to Horizontal Bounding Box.
"""
hbb = np.zeros(4, dtype=np.int32)
hbb[0] = min(obb[:, 0])
hbb[1] = min(obb[:, 1])
hbb[2] = max(obb[:, 0])
hbb[3] = max(obb[:, 1])
return hbb
def expand_list(merged, html_list):
'''
Fill blanks according to merged cells
'''
sr, er, sc, ec = merged
for i in range(sr, er):
for j in range(sc, ec):
html_list[i][j] = None
html_list[sr][sc] = ''
if ec - sc > 1:
html_list[sr][sc] += " colspan={}".format(ec - sc)
if er - sr > 1:
html_list[sr][sc] += " rowspan={}".format(er - sr)
return html_list
def convert_token(html_list):
'''
Convert raw html to label format
'''
token_list = ["<tbody>"]
# final html list:
for row in html_list:
token_list.append("<tr>")
for col in row:
if col == None:
continue
elif col == 'td':
token_list.extend(["<td>", "</td>"])
else:
token_list.append("<td")
if 'colspan' in col:
_, n = col.split('colspan=')
token_list.append(" colspan=\"{}\"".format(n))
if 'rowspan' in col:
_, n = col.split('rowspan=')
token_list.append(" rowspan=\"{}\"".format(n))
token_list.extend([">", "</td>"])
token_list.append("</tr>")
token_list.append("</tbody>")
return token_list
def stepsInfo(lang='en'):
if lang == 'ch':
msg = "1. 安装与运行：使用上述命令安装与运行程序。\n" \
......
PPOCRLabel/resources/strings/strings-en.properties
浏览文件 @ 752a277a
......@@ -84,7 +84,7 @@ mhelp=Help
iconList=Icon List
detectionBoxposition=Detection box position
recognitionResult=Recognition result
creatPolygon=Create Quadrilateral
creatPolygon=Create PolygonBox
rotateLeft=Left turn 90 degrees
rotateRight=Right turn 90 degrees
drawSquares=Draw Squares
......@@ -110,3 +110,6 @@ lockBoxDetail=Lock selected box/Unlock all box
keyListTitle=Key List
keyDialogTip=Enter object label
keyChange=Change Box Key
TableRecognition=Table Recognition
cellreRecognition=Cell Re-Recognition
exportJSON=export JSON(PubTabNet)
PPOCRLabel/resources/strings/strings-zh-CN.properties
浏览文件 @ 752a277a
......@@ -84,7 +84,7 @@ mhelp=帮助
iconList=缩略图
detectionBoxposition=检测框位置
recognitionResult=识别结果
creatPolygon=四点标注
creatPolygon=多边形标注
drawSquares=正方形标注
rotateLeft=图片左旋转90度
rotateRight=图片右旋转90度
......@@ -109,4 +109,7 @@ lockBox=锁定框/解除锁定框
lockBoxDetail=若当前没有框处于锁定状态则锁定选中的框，若存在锁定框则解除所有锁定框的锁定状态
keyListTitle=关键词列表
keyDialogTip=请输入类型名称
keyChange=更改Box关键字类别
\ No newline at end of file
keyChange=更改Box关键字类别
TableRecognition=表格识别
cellreRecognition=单元格重识别
exportJSON=导出表格JSON标注
\ No newline at end of file
README.md
浏览文件 @ 752a277a
......@@ -30,8 +30,6 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools
PaddleOCR support a variety of cutting-edge algorithms related to OCR, and developed industrial featured models/solution [PP-OCR](./doc/doc_en/ppocr_introduction_en.md) and [PP-Structure](./ppstructure/README.md) on this basis, and get through the whole process of data production, model training, compression, inference and deployment.
PaddleOCR also supports metric and model logging during training to [VisualDL](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/03_VisualDL/visualdl_usage_en.html) and [Weights & Biases](https://docs.wandb.ai/).
![](./doc/features_en.png)
> It is recommended to start with the “quick experience” in the document tutorial
......
deploy/pdserving/config.yml
浏览文件 @ 752a277a
......@@ -36,8 +36,8 @@ op:
#det模型路径
model_config: ./ppocr_det_v3_serving
#Fetch结果列表，以client_config中fetch_var的alias_name为准
fetch_list: ["sigmoid_0.tmp_0"]
#Fetch结果列表，以client_config中fetch_var的alias_name为准，不设置默认取全部输出变量
#fetch_list: ["sigmoid_0.tmp_0"]
#计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
devices: "0"
......@@ -62,8 +62,8 @@ op:
#rec模型路径
model_config: ./ppocr_rec_v3_serving
#Fetch结果列表，以client_config中fetch_var的alias_name为准
fetch_list: ["softmax_5.tmp_0"]
#Fetch结果列表，以client_config中fetch_var的alias_name为准, 不设置默认取全部输出变量
#fetch_list:
#计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
devices: "0"
......
deploy/pdserving/ocr_reader.py
浏览文件 @ 752a277a
......@@ -393,7 +393,7 @@ class OCRReader(object):
return norm_img_batch[0]
def postprocess(self, outputs, with_score=False):
preds = outputs["softmax_5.tmp_0"]
preds = list(outputs.values())[0]
try:
preds = preds.numpy()
except:
......@@ -404,8 +404,11 @@ class OCRReader(object):
preds_idx, preds_prob, is_remove_duplicate=True)
return text
from argparse import ArgumentParser,RawDescriptionHelpFormatter
from argparse import ArgumentParser, RawDescriptionHelpFormatter
import yaml
class ArgsParser(ArgumentParser):
def __init__(self):
super(ArgsParser, self).__init__(
......@@ -441,16 +444,16 @@ class ArgsParser(ArgumentParser):
s = s.strip()
k, v = s.split('=')
v = self._parse_helper(v)
print(k,v, type(v))
print(k, v, type(v))
cur = config
parent = cur
for kk in k.split("."):
if kk not in cur:
cur[kk] = {}
parent = cur
cur = cur[kk]
cur[kk] = {}
parent = cur
cur = cur[kk]
else:
parent = cur
cur = cur[kk]
parent = cur
cur = cur[kk]
parent[k.split(".")[-1]] = v
return config
\ No newline at end of file
return config
deploy/pdserving/web_service.py
浏览文件 @ 752a277a
......@@ -56,7 +56,7 @@ class DetOp(Op):
return {"x": det_img[np.newaxis, :].copy()}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, data_id, log_id):
det_out = fetch_dict["sigmoid_0.tmp_0"]
det_out = list(fetch_dict.values())[0]
ratio_list = [
float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
]
......
deploy/pdserving/web_service_det.py
浏览文件 @ 752a277a
......@@ -55,7 +55,7 @@ class DetOp(Op):
return {"x": det_img[np.newaxis, :].copy()}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, data_id, log_id):
det_out = fetch_dict["sigmoid_0.tmp_0"]
det_out = list(fetch_dict.values())[0]
ratio_list = [
float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
]
......
doc/doc_ch/PP-OCRv3_introduction.md
浏览文件 @ 752a277a
......@@ -110,14 +110,15 @@ PP-OCRv3识别模型从网络结构、训练策略、数据增广等多个方面
|-----|-----|--------|----| --- |
| 01 | PP-OCRv2 | 8M | 74.8% | 8.54ms |
| 02 | SVTR_Tiny | 21M | 80.1% | 97ms |
| 03 | SVTR_LCNet | 12M | 71.9% | 6.6ms |
| 04 | + GTC | 12M | 75.8% | 7.6ms |
| 05 | + TextConAug | 12M | 76.3% | 7.6ms |
| 06 | + TextRotNet | 12M | 76.9% | 7.6ms |
| 07 | + UDML | 12M | 78.4% | 7.6ms |
| 08 | + UIM | 12M | 79.4% | 7.6ms |
| 03 | SVTR_LCNet(h32) | 12M | 71.9% | 6.6ms |
| 04 | SVTR_LCNet(h48) | 12M | 73.98% | 7.6ms |
| 05 | + GTC | 12M | 75.8% | 7.6ms |
| 06 | + TextConAug | 12M | 76.3% | 7.6ms |
| 07 | + TextRotNet | 12M | 76.9% | 7.6ms |
| 08 | + UDML | 12M | 78.4% | 7.6ms |
| 09 | + UIM | 12M | 79.4% | 7.6ms |
注： 测试速度时，实验01-03输入图片尺寸均为(3,32,320)，04-08输入图片尺寸均为(3,48,320)。在实际预测时，图像为变长输入，速度会有所变化。
注： 测试速度时，实验01-03输入图片尺寸均为(3,32,320)，04-09输入图片尺寸均为(3,48,320)。在实际预测时，图像为变长输入，速度会有所变化。
**（1）轻量级文本识别网络SVTR_LCNet。**
......@@ -153,9 +154,10 @@ PP-OCRv3将base模型从CRNN替换成了[SVTR](https://arxiv.org/abs/2205.00159)
| 02 | SVTR_Tiny | 21M | 80.1% | 97ms |
| 03 | SVTR_LCNet(G4) | 9.2M | 76% | 30ms |
| 04 | SVTR_LCNet(G2) | 13M | 72.98% | 9.37ms |
| 05 | SVTR_LCNet | 12M | 71.9% | 6.6ms |
| 05 | SVTR_LCNet(h32) | 12M | 71.9% | 6.6ms |
| 06 | SVTR_LCNet(h48) | 12M | 73.98% | 7.6ms |
注： 测试速度时，输入图片尺寸均为(3,32,320)； PP-OCRv2-baseline 代表没有借助蒸馏方法训练得到的模型
注： 测试速度时，01-05输入图片尺寸均为(3,32,320)； PP-OCRv2-baseline 代表没有借助蒸馏方法训练得到的模型
**（2）采用Attention指导CTC训练。**
......@@ -178,7 +180,7 @@ PP-OCRv3将base模型从CRNN替换成了[SVTR](https://arxiv.org/abs/2205.00159)
为了充分利用自然场景中的大量无标注文本数据，PP-OCRv3参考论文[STR-Fewer-Labels](https://github.com/ku21fan/STR-Fewer-Labels)，设计TextRotNet自监督任务，对识别图像进行旋转并预测其旋转角度，同时结合中文场景文字识别任务的特点，在训练时适当调整图像的尺寸，添加文本识别数据增广，最终产出针对文本识别任务的PP-LCNet预训练模型，帮助识别模型精度进一步提升0.6%。TextRotNet训练流程如下图所示：
<div align="center">
<img src="../ppocr_v3/SSL.png" width="500">
<img src="../ppocr_v3/SSL.png" width="500">
</div>
......@@ -187,7 +189,7 @@ PP-OCRv3将base模型从CRNN替换成了[SVTR](https://arxiv.org/abs/2205.00159)
为更直接利用自然场景中包含大量无标注数据，使用PP-OCRv2检测模型以及SVTR_tiny识别模型对百度开源的40W [LSVT弱标注数据集](https://ai.baidu.com/broad/introduction?dataset=lsvt)进行检测与识别，并筛选出识别得分大于0.95的文本，共81W文本行数据，将其补充到训练数据中，最终进一步提升模型精度1.0%。
<div align="center">
<img src="../ppocr_v3/UIM.png" width="500">
<img src="../ppocr_v3/UIM.png" width="500">
</div>
......
doc/doc_ch/models_list.md
浏览文件 @ 752a277a
......@@ -18,13 +18,13 @@
- [3. 文本方向分类模型](#3-文本方向分类模型)
- [4. Paddle-Lite 模型](#4-paddle-lite-模型)
PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训练模型`、`slim模型`，模型区别说明如下：
PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训练模型`、`nb模型`，模型区别说明如下：
|模型类型|模型格式|简介|
|--- | --- | --- |
|推理模型|inference.pdmodel、inference.pdiparams|用于预测引擎推理，[详情](./inference.md)|
|训练模型、预训练模型|\*.pdparams、\*.pdopt、\*.states |训练过程中保存的模型的参数、优化器状态和训练中间信息，多用于模型指标评估和恢复训练|
|slim模型|\*.nb|经过飞桨模型压缩工具PaddleSlim压缩后的模型，适用于移动端/IoT端等端侧部署场景（需使用飞桨Paddle Lite部署）。|
|nb模型|\*.nb|经过飞桨Paddle-Lite工具优化后的模型，适用于移动端/IoT端等端侧部署场景（需使用飞桨Paddle Lite部署）。|
各个模型的关系如下面的示意图所示。
......@@ -41,7 +41,7 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
|模型名称|模型简介|配置文件|推理模型大小|下载地址|
| --- | --- | --- | --- | --- |
|ch_PP-OCRv3_det_slim|【最新】slim量化+蒸馏版超轻量模型，支持中英文、多语种文本检测|[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 1.1M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_distill_train.tar) / [slim模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.nb)|
|ch_PP-OCRv3_det_slim|【最新】slim量化+蒸馏版超轻量模型，支持中英文、多语种文本检测|[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 1.1M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_distill_train.tar) / [nb模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.nb)|
|ch_PP-OCRv3_det| 【最新】原始超轻量模型，支持中英文、多语种文本检测 |[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 3.8M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar)|
|ch_PP-OCRv2_det_slim| slim量化+蒸馏版超轻量模型，支持中英文、多语种文本检测|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)| 3M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar)|
|ch_PP-OCRv2_det| 原始超轻量模型，支持中英文、多语种文本检测|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)|3M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)|
......@@ -55,8 +55,8 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
|模型名称|模型简介|配置文件|推理模型大小|下载地址|
| --- | --- | --- | --- | --- |
|en_PP-OCRv3_det_slim |【最新】slim量化版超轻量模型，支持英文、数字检测 | [ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml) | 1.1M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_slim_distill_train.tar) / [slim模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_slim_infer.nb) |
|ch_PP-OCRv3_det |【最新】原始超轻量模型，支持英文、数字检测|[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 3.8M | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_distill_train.tar) |
|en_PP-OCRv3_det_slim |【最新】slim量化版超轻量模型，支持英文、数字检测 | [ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml) | 1.1M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_slim_distill_train.tar) / [nb模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_slim_infer.nb) |
|en_PP-OCRv3_det |【最新】原始超轻量模型，支持英文、数字检测|[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 3.8M | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_distill_train.tar) |
* 注：英文检测模型与中文检测模型结构完全相同，只有训练数据不同，在此仅提供相同的配置文件。
......@@ -66,7 +66,7 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
|模型名称|模型简介|配置文件|推理模型大小|下载地址|
| --- | --- | --- | --- | --- |
| ml_PP-OCRv3_det_slim |【最新】slim量化版超轻量模型，支持多语言检测 | [ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml) | 1.1M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_slim_distill_train.tar) / [slim模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_slim_infer.nb) |
| ml_PP-OCRv3_det_slim |【最新】slim量化版超轻量模型，支持多语言检测 | [ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml) | 1.1M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_slim_distill_train.tar) / [nb模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_slim_infer.nb) |
| ml_PP-OCRv3_det |【最新】原始超轻量模型，支持多语言检测 | [ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 3.8M | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_distill_train.tar) |
* 注：多语言检测模型与中文检测模型结构完全相同，只有训练数据不同，在此仅提供相同的配置文件。
......@@ -81,7 +81,7 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
|模型名称|模型简介|配置文件|推理模型大小|下载地址|
| --- | --- | --- | --- | --- |
|ch_PP-OCRv3_rec_slim |【最新】slim量化版超轻量模型，支持中英文、数字识别|[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)| 4.9M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_train.tar) / [slim模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.nb) |
|ch_PP-OCRv3_rec_slim |【最新】slim量化版超轻量模型，支持中英文、数字识别|[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)| 4.9M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_train.tar) / [nb模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.nb) |
|ch_PP-OCRv3_rec|【最新】原始超轻量模型，支持中英文、数字识别|[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)| 12.4M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar) |
|ch_PP-OCRv2_rec_slim| slim量化版超轻量模型，支持中英文、数字识别|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)| 9M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_train.tar) |
|ch_PP-OCRv2_rec| 原始超轻量模型，支持中英文、数字识别|[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)|8.5M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) |
......@@ -96,8 +96,8 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
|模型名称|模型简介|配置文件|推理模型大小|下载地址|
| --- | --- | --- | --- | --- |
|en_PP-OCRv3_rec_slim |【最新】slim量化版超轻量模型，支持英文、数字识别 | [en_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml)| - |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_train.tar) / [slim模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_infer.nb) |
|ch_PP-OCRv3_rec |【最新】原始超轻量模型，支持英文、数字识别|[en_PP-OCRv3_rec.yml](../../configs/rec/en_PP-OCRv3/en_PP-OCRv3_rec.yml)| 9.6M | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) |
|en_PP-OCRv3_rec_slim |【最新】slim量化版超轻量模型，支持英文、数字识别 | [en_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml)| 3.2M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_train.tar) / [nb模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_infer.nb) |
|en_PP-OCRv3_rec |【最新】原始超轻量模型，支持英文、数字识别|[en_PP-OCRv3_rec.yml](../../configs/rec/en_PP-OCRv3/en_PP-OCRv3_rec.yml)| 9.6M | [推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) |
|en_number_mobile_slim_v2.0_rec|slim裁剪量化版超轻量模型，支持英文、数字识别|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)| 2.7M | [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/en_number_mobile_v2.0_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/en_number_mobile_v2.0_rec_slim_train.tar) |
|en_number_mobile_v2.0_rec|原始超轻量模型，支持英文、数字识别|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)|2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_train.tar) |
......@@ -107,18 +107,16 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
|模型名称|字典文件|模型简介|配置文件|推理模型大小|下载地址|
| --- | --- | --- | --- |--- | --- |
| korean_PP-OCRv3_rec | ppocr/utils/dict/korean_dict.txt |韩文识别|[korean_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/korean_PP-OCRv3_rec.yml)|11M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_PP-OCRv3_rec_train.tar) |
| japan_PP-OCRv3_rec | ppocr/utils/dict/japan_dict.txt |日文识别|[japan_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/japan_PP-OCRv3_rec.yml)|11M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_PP-OCRv3_rec_train.tar) |
| chinese_cht_PP-OCRv3_rec | ppocr/utils/dict/chinese_cht_dict.txt | 中文繁体识别|[chinese_cht_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/chinese_cht_PP-OCRv3_rec.yml)|12M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_PP-OCRv3_rec_train.tar) |
| te_PP-OCRv3_rec | ppocr/utils/dict/te_dict.txt | 泰卢固文识别|[te_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/te_PP-OCRv3_rec.yml)|9.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_PP-OCRv3_rec_train.tar) |
| ka_PP-OCRv3_rec | ppocr/utils/dict/ka_dict.txt |卡纳达文识别|[ka_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/ka_PP-OCRv3_rec.yml)|9.9M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_PP-OCRv3_rec_train.tar) |
| ta_PP-OCRv3_rec | ppocr/utils/dict/ta_dict.txt |泰米尔文识别|[ta_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/ta_PP-OCRv3_rec.yml)|9.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_PP-OCRv3_rec_train.tar) |
| latin_PP-OCRv3_rec | ppocr/utils/dict/latin_dict.txt | 拉丁文识别 | [latin_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/latin_PP-OCRv3_rec.yml) |9.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_PP-OCRv3_rec_train.tar) |
| arabic_PP-OCRv3_rec | ppocr/utils/dict/arabic_dict.txt | 阿拉伯字母 | [arabic_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/rec_arabic_lite_train.yml) |9.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_PP-OCRv3_rec_train.tar) |
| cyrillic_PP-OCRv3_rec | ppocr/utils/dict/cyrillic_dict.txt | 斯拉夫字母 | [cyrillic_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/cyrillic_PP-OCRv3_rec.yml) |9.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_PP-OCRv3_rec_train.tar) |
| devanagari_PP-OCRv3_rec | ppocr/utils/dict/devanagari_dict.txt |梵文字母 | [devanagari_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/devanagari_PP-OCRv3_rec.yml) |9.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_PP-OCRv3_rec_train.tar) |
| korean_PP-OCRv3_rec | ppocr/utils/dict/korean_dict.txt |韩文识别|[korean_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/korean_PP-OCRv3_rec.yml)|11M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/korean_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/korean_PP-OCRv3_rec_train.tar) |
| japan_PP-OCRv3_rec | ppocr/utils/dict/japan_dict.txt |日文识别|[japan_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/japan_PP-OCRv3_rec.yml)|11M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/japan_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/japan_PP-OCRv3_rec_train.tar) |
| chinese_cht_PP-OCRv3_rec | ppocr/utils/dict/chinese_cht_dict.txt | 中文繁体识别|[chinese_cht_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/chinese_cht_PP-OCRv3_rec.yml)|12M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_train.tar) |
| te_PP-OCRv3_rec | ppocr/utils/dict/te_dict.txt | 泰卢固文识别|[te_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/te_PP-OCRv3_rec.yml)|9.6M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/te_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/te_PP-OCRv3_rec_train.tar) |
| ka_PP-OCRv3_rec | ppocr/utils/dict/ka_dict.txt |卡纳达文识别|[ka_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/ka_PP-OCRv3_rec.yml)|9.9M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_train.tar) |
| ta_PP-OCRv3_rec | ppocr/utils/dict/ta_dict.txt |泰米尔文识别|[ta_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/ta_PP-OCRv3_rec.yml)|9.6M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_train.tar) |
| latin_PP-OCRv3_rec | ppocr/utils/dict/latin_dict.txt | 拉丁文识别 | [latin_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/latin_PP-OCRv3_rec.yml) |9.7M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/latin_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/latin_PP-OCRv3_rec_train.tar) |
| arabic_PP-OCRv3_rec | ppocr/utils/dict/arabic_dict.txt | 阿拉伯字母 | [arabic_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/rec_arabic_lite_train.yml) |9.6M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_train.tar) |
| cyrillic_PP-OCRv3_rec | ppocr/utils/dict/cyrillic_dict.txt | 斯拉夫字母 | [cyrillic_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/cyrillic_PP-OCRv3_rec.yml) |9.6M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/cyrillic_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/cyrillic_PP-OCRv3_rec_train.tar) |
| devanagari_PP-OCRv3_rec | ppocr/utils/dict/devanagari_dict.txt |梵文字母 | [devanagari_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/multi_language/devanagari_PP-OCRv3_rec.yml) |9.9M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/devanagari_PP-OCRv3_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/devanagari_PP-OCRv3_rec_train.tar) |
更多支持语种请参考: [多语言模型](./multi_languages.md)
......@@ -128,13 +126,18 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
|模型名称|模型简介|配置文件|推理模型大小|下载地址|
| --- | --- | --- | --- | --- |
|ch_ppocr_mobile_slim_v2.0_cls|slim量化版模型，对检测到的文本行文字角度分类|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)| 2.1M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) |
|ch_ppocr_mobile_slim_v2.0_cls|slim量化版模型，对检测到的文本行文字角度分类|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)| 2.1M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [nb模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb) |
|ch_ppocr_mobile_v2.0_cls|原始分类器模型，对检测到的文本行文字角度分类|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|1.38M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |
<a name="Paddle-Lite模型"></a>
## 4. Paddle-Lite 模型
Paddle-Lite 是一个高性能、轻量级、灵活性强且易于扩展的深度学习推理框架，它可以对inference模型进一步优化，得到适用于移动端/IoT端等端侧部署场景的`nb模型`。一般建议基于量化模型进行转换，因为可以将模型以INT8形式进行存储与推理，从而进一步减小模型大小，提升模型速度。
本节主要列出PP-OCRv2以及更早版本的检测与识别nb模型，最新版本的nb模型可以直接从上面的模型列表中获得。
|模型版本|模型简介|模型大小|检测模型|文本方向分类模型|识别模型|Paddle-Lite版本|
|---|---|---|---|---|---|---|
|PP-OCRv2|蒸馏版超轻量中文OCR移动端模型|11M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
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doc/doc_ch/quickstart.md
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......@@ -59,15 +59,13 @@ cd /path/to/ppocr_img
如果不使用提供的测试图片，可以将下方`--image_dir`参数替换为相应的测试图片路径。
**注意** whl包默认使用`PP-OCRv3`模型，识别模型使用的输入shape为`3,48,320`, 因此如果使用识别功能，需要添加参数`--rec_image_shape 3,48,320`，如果不使用默认的`PP-OCRv3`模型，则无需设置该参数。
<a name="211"></a>
#### 2.1.1 中英文模型
* 检测+方向分类器+识别全流程：`--use_angle_cls true`设置使用方向分类器识别180度旋转文字，`--use_gpu false`设置不使用GPU
```bash
paddleocr --image_dir ./imgs/11.jpg --use_angle_cls true --use_gpu false --rec_image_shape 3,48,320
paddleocr --image_dir ./imgs/11.jpg --use_angle_cls true --use_gpu false
```
结果是一个list，每个item包含了文本框，文字和识别置信度
......@@ -94,7 +92,7 @@ cd /path/to/ppocr_img
- 单独使用识别：设置`--det`为`false`
```bash
paddleocr --image_dir ./imgs_words/ch/word_1.jpg --det false --rec_image_shape 3,48,320
paddleocr --image_dir ./imgs_words/ch/word_1.jpg --det false
```
结果是一个list，每个item只包含识别结果和识别置信度
......@@ -104,16 +102,16 @@ cd /path/to/ppocr_img
```
如需使用2.0模型，请指定参数`--version PP-OCR`，paddleocr默认使用PP-OCRv3模型(`--versioin PP-OCRv3`)。更多whl包使用可参考[whl包文档](./whl.md)
如需使用2.0模型，请指定参数`--ocr_version PP-OCR`，paddleocr默认使用PP-OCRv3模型(`--ocr_version PP-OCRv3`)。更多whl包使用可参考[whl包文档](./whl.md)
<a name="212"></a>
#### 2.1.2 多语言模型
PaddleOCR目前支持80个语种，可以通过修改`--lang`参数进行切换，对于英文模型，指定`--lang=en`, PP-OCRv3目前只支持中文和英文模型，其他多语言模型会陆续更新。
PaddleOCR目前支持80个语种，可以通过修改`--lang`参数进行切换，对于英文模型，指定`--lang=en`。
``` bash
paddleocr --image_dir ./imgs_en/254.jpg --lang=en --rec_image_shape 3,48,320
paddleocr --image_dir ./imgs_en/254.jpg --lang=en
```
<div align="center">
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doc/doc_ch/update.md
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# 更新
- 2022.5.7 添加对[Weights & Biases](https://docs.wandb.ai/)训练日志记录工具的支持。
- 2021.12.21 《OCR十讲》课程开讲，12月21日起每晚八点半线上授课！ 【免费】报名地址：https://aistudio.baidu.com/aistudio/course/introduce/25207
- 2021.12.21 发布PaddleOCR v2.4。OCR算法新增1种文本检测算法（PSENet），3种文本识别算法（NRTR、SEED、SAR）；文档结构化算法新增1种关键信息提取算法（SDMGR），3种DocVQA算法（LayoutLM、LayoutLMv2，LayoutXLM）。
- 2021.9.7 发布PaddleOCR v2.3，发布[PP-OCRv2](#PP-OCRv2)，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。
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doc/doc_ch/whl.md
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......@@ -199,12 +199,10 @@ for line in result:
paddleocr -h
```
**注意** whl包默认使用`PP-OCRv3`模型，识别模型使用的输入shape为`3,48,320`, 因此如果使用识别功能，需要添加参数`--rec_image_shape 3,48,320`，如果不使用默认的`PP-OCRv3`模型，则无需设置该参数。
* 检测+方向分类器+识别全流程
```bash
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true --rec_image_shape 3,48,320
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true
```
结果是一个list，每个item包含了文本框，文字和识别置信度
......@@ -217,7 +215,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true --rec_image
* 检测+识别
```bash
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec_image_shape 3,48,320
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg
```
结果是一个list，每个item包含了文本框，文字和识别置信度
......@@ -230,7 +228,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec_image_shape 3,48,320
* 方向分类器+识别
```bash
paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --det false --rec_image_shape 3,48,320
paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --det false
```
结果是一个list，每个item只包含识别结果和识别置信度
......@@ -256,7 +254,7 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
* 单独执行识别
```bash
paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --det false --rec_image_shape 3,48,320
paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --det false
```
结果是一个list，每个item只包含识别结果和识别置信度
......@@ -416,4 +414,4 @@ im_show.save('result.jpg')
| cls | 前向时是否启动分类 (命令行模式下使用use_angle_cls控制前向是否启动分类) | FALSE |
| show_log | 是否打印logger信息 | FALSE |
| type | 执行ocr或者表格结构化, 值可选['ocr','structure'] | ocr |
| ocr_version | OCR模型版本，可选PP-OCRv3, PP-OCRv2, PP-OCR。PP-OCRv3 目前仅支持中、英文的检测和识别模型，方向分类器模型；PP-OCRv2 目前仅支持中文的检测和识别模型；PP-OCR支持中文的检测，识别，多语种识别，方向分类器等模型 | PP-OCRv3 |
| ocr_version | OCR模型版本，可选PP-OCRv3, PP-OCRv2, PP-OCR。PP-OCRv3 支持中、英文的检测、识别、多语种识别，方向分类器等模型；PP-OCRv2 目前仅支持中文的检测和识别模型；PP-OCR支持中文的检测，识别，多语种识别，方向分类器等模型 | PP-OCRv3 |
doc/doc_en/models_list_en.md
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- [3. Text Angle Classification Model](#3-text-angle-classification-model)
- [4. Paddle-Lite Model](#4-paddle-lite-model)
The downloadable models provided by PaddleOCR include `inference model`, `trained model`, `pre-trained model` and `slim model`. The differences between the models are as follows:
The downloadable models provided by PaddleOCR include `inference model`, `trained model`, `pre-trained model` and `nb model`. The differences between the models are as follows:
|model type|model format|description|
|--- | --- | --- |
|inference model|inference.pdmodel、inference.pdiparams|Used for inference based on Paddle inference engine，[detail](./inference_en.md)|
|trained model, pre-trained model|\*.pdparams、\*.pdopt、\*.states |The checkpoints model saved in the training process, which stores the parameters of the model, mostly used for model evaluation and continuous training.|
|slim model|\*.nb| Model compressed by PaddleSlim (a model compression tool using PaddlePaddle), which is suitable for mobile-side deployment scenarios (Paddle-Lite is needed for slim model deployment). |
|nb model|\*.nb| Model optimized by Paddle-Lite, which is suitable for mobile-side deployment scenarios (Paddle-Lite is needed for nb model deployment). |
Relationship of the above models is as follows.
......@@ -37,7 +37,7 @@ Relationship of the above models is as follows.
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
|ch_PP-OCRv3_det_slim| [New] slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection |[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 1.1M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/ch/ch_PP-OCRv3_det_slim_distill_train.tar) / [slim model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.nb)|
|ch_PP-OCRv3_det_slim| [New] slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection |[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 1.1M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/ch/ch_PP-OCRv3_det_slim_distill_train.tar) / [nb model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.nb)|
|ch_PP-OCRv3_det| [New] Original lightweight model, supporting Chinese, English, multilingual text detection |[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 3.8M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar)|
|ch_PP-OCRv2_det_slim| [New] slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)| 3M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar)|
|ch_PP-OCRv2_det| [New] Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml)|3M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)|
......@@ -51,7 +51,7 @@ Relationship of the above models is as follows.
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
|en_PP-OCRv3_det_slim | [New] Slim qunatization with distillation lightweight detection model, supporting English | [ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml) | 1.1M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_slim_distill_train.tar) / [slim model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_slim_infer.nb) |
|en_PP-OCRv3_det_slim | [New] Slim qunatization with distillation lightweight detection model, supporting English | [ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml) | 1.1M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_slim_distill_train.tar) / [nb model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_slim_infer.nb) |
|ch_PP-OCRv3_det | [New] Original lightweight detection model, supporting English |[ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 3.8M | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_distill_train.tar) |
* Note: English configuration file is same as Chinese except training data, here we only provide one configuration file.
......@@ -62,7 +62,7 @@ Relationship of the above models is as follows.
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
| ml_PP-OCRv3_det_slim | [New] Slim qunatization with distillation lightweight detection model, supporting English | [ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml) | 1.1M | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_slim_infer.tar) / [trained model ](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_slim_distill_train.tar) / [slim model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_slim_infer.nb) |
| ml_PP-OCRv3_det_slim | [New] Slim qunatization with distillation lightweight detection model, supporting English | [ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml) | 1.1M | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_slim_infer.tar) / [trained model ](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_slim_distill_train.tar) / [nb model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_slim_infer.nb) |
| ml_PP-OCRv3_det |[New] Original lightweight detection model, supporting English | [ch_PP-OCRv3_det_cml.yml](../../configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml)| 3.8M | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_distill_train.tar) |
* Note: English configuration file is same as Chinese except training data, here we only provide one configuration file.
......@@ -75,7 +75,7 @@ Relationship of the above models is as follows.
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
|ch_PP-OCRv3_rec_slim | [New] Slim qunatization with distillation lightweight model, supporting Chinese, English text recognition |[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)| 4.9M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/ch/ch_PP-OCRv3_rec_slim_train.tar) / [slim model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.nb) |
|ch_PP-OCRv3_rec_slim | [New] Slim qunatization with distillation lightweight model, supporting Chinese, English text recognition |[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)| 4.9M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/ch/ch_PP-OCRv3_rec_slim_train.tar) / [nb model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.nb) |
|ch_PP-OCRv3_rec| [New] Original lightweight model, supporting Chinese, English, multilingual text recognition |[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml)| 12.4M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar) |
|ch_PP-OCRv2_rec_slim| Slim qunatization with distillation lightweight model, supporting Chinese, English text recognition|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)| 9M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_train.tar) |
|ch_PP-OCRv2_rec| Original lightweight model, supporting Chinese, English, multilingual text recognition |[ch_PP-OCRv2_rec_distillation.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml)|8.5M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) |
......@@ -91,8 +91,8 @@ Relationship of the above models is as follows.
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
|en_PP-OCRv3_rec_slim | [New] Slim qunatization with distillation lightweight model, supporting english, English text recognition |[en_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/en_PP-OCRv3_rec_distillation.yml)| 4.9M |[inference model(coming soon)](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_infer.tar) / [trained model (coming soon)](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_train.tar) / [slim model(coming soon)](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_infer.nb) |
|en_PP-OCRv3_rec| [New] Original lightweight model, supporting english, English, multilingual text recognition |[en_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/en_PP-OCRv3_rec_distillation.yml)| 12.4M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) |
|en_PP-OCRv3_rec_slim | [New] Slim qunatization with distillation lightweight model, supporting english, English text recognition |[en_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml)| 3.2M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_train.tar) / [nb model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_slim_infer.nb) |
|en_PP-OCRv3_rec| [New] Original lightweight model, supporting english, English, multilingual text recognition |[en_PP-OCRv3_rec.yml](../../configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml)| 9.6M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) |
|en_number_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting English and number recognition|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)| 2.7M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/en_number_mobile_v2.0_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/en_number_mobile_v2.0_rec_slim_train.tar) |
|en_number_mobile_v2.0_rec|Original lightweight model, supporting English and number recognition|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)|2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_train.tar) |
......@@ -122,11 +122,16 @@ For more supported languages, please refer to : [Multi-language model](./multi_l
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
|ch_ppocr_mobile_slim_v2.0_cls|Slim quantized model for text angle classification|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)| 2.1M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_train.tar) |
|ch_ppocr_mobile_slim_v2.0_cls|Slim quantized model for text angle classification|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)| 2.1M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_train.tar) / [nb model](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb) |
|ch_ppocr_mobile_v2.0_cls|Original model for text angle classification|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|1.38M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |
<a name="Paddle-Lite"></a>
## 4. Paddle-Lite Model
Paddle Lite is an updated version of Paddle-Mobile, an open-open source deep learning framework designed to make it easy to perform inference on mobile, embeded, and IoT devices. It can further optimize the inference model and generate `nb model` used for edge devices. It's suggested to optimize the quantization model using Paddle-Lite because `INT8` format is used for the model storage and inference.
This chapter lists OCR nb models with PP-OCRv2 or earlier versions. You can access to the latest nb models from the above tables.
|Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle-Lite branch|
|---|---|---|---|---|---|---|
|PP-OCRv2|extra-lightweight chinese OCR optimized model|11M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_ppocr_mobile_v2.0_cls_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/lite/ch_PP-OCRv2_rec_infer_opt.nb)|v2.10|
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doc/doc_en/quickstart_en.md
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......@@ -73,8 +73,6 @@ cd /path/to/ppocr_img
If you do not use the provided test image, you can replace the following `--image_dir` parameter with the corresponding test image path
**Note**: The whl package uses the `PP-OCRv3` model by default, and the input shape used by the recognition model is `3,48,320`, so if you use the recognition function, you need to add the parameter `--rec_image_shape 3,48,320`, if you do not use the default `PP- OCRv3` model, you do not need to set this parameter.
<a name="211-english-and-chinese-model"></a>
#### 2.1.1 Chinese and English Model
......@@ -82,7 +80,7 @@ If you do not use the provided test image, you can replace the following `--imag
* Detection, direction classification and recognition: set the parameter`--use_gpu false` to disable the gpu device
```bash
paddleocr --image_dir ./imgs_en/img_12.jpg --use_angle_cls true --lang en --use_gpu false --rec_image_shape 3,48,320
paddleocr --image_dir ./imgs_en/img_12.jpg --use_angle_cls true --lang en --use_gpu false
```
Output will be a list, each item contains bounding box, text and recognition confidence
......@@ -112,7 +110,7 @@ If you do not use the provided test image, you can replace the following `--imag
* Only recognition: set `--det` to `false`
```bash
paddleocr --image_dir ./imgs_words_en/word_10.png --det false --lang en --rec_image_shape 3,48,320
paddleocr --image_dir ./imgs_words_en/word_10.png --det false --lang en
```
Output will be a list, each item contains text and recognition confidence
......@@ -121,15 +119,15 @@ If you do not use the provided test image, you can replace the following `--imag
['PAIN', 0.9934559464454651]
```
If you need to use the 2.0 model, please specify the parameter `--version PP-OCR`, paddleocr uses the PP-OCRv3 model by default(`--versioin PP-OCRv3`). More whl package usage can be found in [whl package](./whl_en.md)
If you need to use the 2.0 model, please specify the parameter `--ocr_version PP-OCR`, paddleocr uses the PP-OCRv3 model by default(`--ocr_version PP-OCRv3`). More whl package usage can be found in [whl package](./whl_en.md)
<a name="212-multi-language-model"></a>
#### 2.1.2 Multi-language Model
PaddleOCR currently supports 80 languages, which can be switched by modifying the `--lang` parameter. PP-OCRv3 currently only supports Chinese and English models, and other multilingual models will be updated one after another.
PaddleOCR currently supports 80 languages, which can be switched by modifying the `--lang` parameter.
``` bash
paddleocr --image_dir ./doc/imgs_en/254.jpg --lang=en --rec_image_shape 3,48,320
paddleocr --image_dir ./doc/imgs_en/254.jpg --lang=en
```
<div align="center">
......@@ -210,4 +208,4 @@ Visualization of results
In this section, you have mastered the use of PaddleOCR whl package.
PaddleOCR is a rich and practical OCR tool library that get through the whole process of data production, model training, compression, inference and deployment, please refer to the [tutorials](../../README.md#tutorials) to start the journey of PaddleOCR.
\ No newline at end of file
PaddleOCR is a rich and practical OCR tool library that get through the whole process of data production, model training, compression, inference and deployment, please refer to the [tutorials](../../README.md#tutorials) to start the journey of PaddleOCR.
doc/doc_en/update_en.md
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# RECENT UPDATES
- 2022.5.7 Add support for metric and model logging during training to [Weights & Biases](https://docs.wandb.ai/).
- 2021.12.21 OCR open source online course starts. The lesson starts at 8:30 every night and lasts for ten days. Free registration: https://aistudio.baidu.com/aistudio/course/introduce/25207
- 2021.12.21 release PaddleOCR v2.4, release 1 text detection algorithm (PSENet), 3 text recognition algorithms (NRTR、SEED、SAR), 1 key information extraction algorithm (SDMGR) and 3 DocVQA algorithms (LayoutLM、LayoutLMv2，LayoutXLM).
- 2021.9.7 release PaddleOCR v2.3, [PP-OCRv2](#PP-OCRv2) is proposed. The CPU inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile.
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doc/doc_en/whl_en.md
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......@@ -172,11 +172,9 @@ show help information
paddleocr -h
```
**Note**: The whl package uses the `PP-OCRv3` model by default, and the input shape used by the recognition model is `3,48,320`, so if you use the recognition function, you need to add the parameter `--rec_image_shape 3,48,320`, if you do not use the default `PP- OCRv3` model, you do not need to set this parameter.
* detection classification and recognition
```bash
paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --use_angle_cls true --lang en --rec_image_shape 3,48,320
paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --use_angle_cls true --lang en
```
Output will be a list, each item contains bounding box, text and recognition confidence
......@@ -189,7 +187,7 @@ Output will be a list, each item contains bounding box, text and recognition con
* detection and recognition
```bash
paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --lang en --rec_image_shape 3,48,320
paddleocr --image_dir PaddleOCR/doc/imgs_en/img_12.jpg --lang en
```
Output will be a list, each item contains bounding box, text and recognition confidence
......@@ -202,7 +200,7 @@ Output will be a list, each item contains bounding box, text and recognition con
* classification and recognition
```bash
paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --use_angle_cls true --det false --lang en --rec_image_shape 3,48,320
paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --use_angle_cls true --det false --lang en
```
Output will be a list, each item contains text and recognition confidence
......@@ -225,7 +223,7 @@ Output will be a list, each item only contains bounding box
* only recognition
```bash
paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --det false --lang en --rec_image_shape 3,48,320
paddleocr --image_dir PaddleOCR/doc/imgs_words_en/word_10.png --det false --lang en
```
Output will be a list, each item contains text and recognition confidence
......@@ -368,4 +366,4 @@ im_show.save('result.jpg')
| cls | Enable classification when `ppocr.ocr` func exec((Use use_angle_cls in command line mode to control whether to start classification in the forward direction) | FALSE |
| show_log | Whether to print log| FALSE |
| type | Perform ocr or table structuring, the value is selected in ['ocr','structure'] | ocr |
| ocr_version | OCR Model version number, the current model support list is as follows: PP-OCRv3 support Chinese and English detection and recognition model and direction classifier model, PP-OCRv2 support Chinese detection and recognition model, PP-OCR support Chinese detection, recognition and direction classifier, multilingual recognition model | PP-OCRv3 |
| ocr_version | OCR Model version number, the current model support list is as follows: PP-OCRv3 supports Chinese and English detection, recognition, multilingual recognition, direction classifier models, PP-OCRv2 support Chinese detection and recognition model, PP-OCR support Chinese detection, recognition and direction classifier, multilingual recognition model | PP-OCRv3 |
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......@@ -67,6 +67,10 @@ MODEL_URLS = {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar',
},
'ml': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/Multilingual_PP-OCRv3_det_infer.tar'
}
},
'rec': {
'ch': {
......@@ -79,6 +83,56 @@ MODEL_URLS = {
'https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/en_dict.txt'
},
'korean': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/korean_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/korean_dict.txt'
},
'japan': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/japan_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/japan_dict.txt'
},
'chinese_cht': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/chinese_cht_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
},
'ta': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ta_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ta_dict.txt'
},
'te': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/te_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/te_dict.txt'
},
'ka': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/ka_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ka_dict.txt'
},
'latin': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/latin_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/latin_dict.txt'
},
'arabic': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/arabic_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/arabic_dict.txt'
},
'cyrillic': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/cyrillic_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
},
'devanagari': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv3/multilingual/devanagari_PP-OCRv3_rec_infer.tar',
'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
},
},
'cls': {
'ch': {
......@@ -259,7 +313,7 @@ def parse_lang(lang):
'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga', 'hr',
'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms', 'mt', 'nl',
'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk', 'sl', 'sq', 'sv',
'sw', 'tl', 'tr', 'uz', 'vi'
'sw', 'tl', 'tr', 'uz', 'vi', 'french', 'german'
]
arabic_lang = ['ar', 'fa', 'ug', 'ur']
cyrillic_lang = [
......@@ -285,8 +339,10 @@ def parse_lang(lang):
det_lang = "ch"
elif lang == 'structure':
det_lang = 'structure'
else:
elif lang in ["en", "latin"]:
det_lang = "en"
else:
det_lang = "ml"
return lang, det_lang
......@@ -356,6 +412,10 @@ class PaddleOCR(predict_system.TextSystem):
params.cls_model_dir, cls_url = confirm_model_dir_url(
params.cls_model_dir,
os.path.join(BASE_DIR, 'whl', 'cls'), cls_model_config['url'])
if params.ocr_version == 'PP-OCRv3':
params.rec_image_shape = "3, 48, 320"
else:
params.rec_image_shape = "3, 32, 320"
# download model
maybe_download(params.det_model_dir, det_url)
maybe_download(params.rec_model_dir, rec_url)
......
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