Merge branch 'dygraph' into dygraph

PPOCRLabel/libs/dataPartitionDialog.py

+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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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/quickstart.md

@@ -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">

doc/doc_ch/update.md

 # 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%。

doc/doc_ch/whl.md

@@ -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

@@ -16,13 +16,13 @@
   - [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|

doc/doc_en/quickstart_en.md

@@ -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

 # 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.

doc/doc_en/whl_en.md

@@ -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                 |

paddleocr.py

@@ -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)