Merge remote-tracking branch 'origin/dygraph' into dy1

configs/rec/PP-OCRv3/multi_language/arabic_PP-OCRv3_rec.yml

@@ -12,7 +12,7 @@ Global:
   checkpoints:
   save_inference_dir:
   use_visualdl: false
-  infer_img: doc/imgs_words/ch/word_1.jpg
+  infer_img: ./doc/imgs_words/arabic/ar_2.jpg
   character_dict_path: ppocr/utils/dict/arabic_dict.txt
   max_text_length: &max_text_length 25
   infer_mode: false

ppocr/modeling/backbones/vqa_layoutlm.py

@@ -29,14 +29,14 @@ __all__ = ["LayoutXLMForSer", "LayoutLMForSer"]
 pretrained_model_dict = {
     LayoutXLMModel: {
         "base": "layoutxlm-base-uncased",
-        "vi": "layoutxlm-wo-backbone-base-uncased",
+        "vi": "vi-layoutxlm-base-uncased",
     },
     LayoutLMModel: {
         "base": "layoutlm-base-uncased",
     },
     LayoutLMv2Model: {
         "base": "layoutlmv2-base-uncased",
-        "vi": "layoutlmv2-wo-backbone-base-uncased",
+        "vi": "vi-layoutlmv2-base-uncased",
     },
 }
 

ppocr/postprocess/rec_postprocess.py

@@ -45,6 +45,27 @@ class BaseRecLabelDecode(object):
             self.dict[char] = i
         self.character = dict_character
 
+        if 'arabic' in character_dict_path:
+            self.reverse = True
+        else:
+            self.reverse = False
+
+    def pred_reverse(self, pred):
+        pred_re = []
+        c_current = ''
+        for c in pred:
+            if not bool(re.search('[a-zA-Z0-9 :*./%+-]', c)):
+                if c_current != '':
+                    pred_re.append(c_current)
+                pred_re.append(c)
+                c_current = ''
+            else:
+                c_current += c
+        if c_current != '':
+            pred_re.append(c_current)
+
+        return ''.join(pred_re[::-1])
+
     def add_special_char(self, dict_character):
         return dict_character
 
@@ -73,6 +94,10 @@ class BaseRecLabelDecode(object):
                 conf_list = [0]
 
             text = ''.join(char_list)
+
+            if self.reverse:  # for arabic rec
+                text = self.pred_reverse(text)
+
             result_list.append((text, np.mean(conf_list).tolist()))
         return result_list
 

ppocr/utils/dict/arabic_dict.txt

- 
 !
 #
 $

ppstructure/docs/models_list_en.md

@@ -13,7 +13,7 @@
 |model name| description                                                                                                                                             | inference model size                                                                                                                         |download|dict path|
 | --- |---------------------------------------------------------------------------------------------------------------------------------------------------------| --- | --- | --- |
 | picodet_lcnet_x1_0_fgd_layout | The layout analysis English model trained on the PubLayNet dataset based on PicoDet LCNet_x1_0 and FGD . the model can recognition 5 types of areas such as **Text, Title, Table, Picture and List** | 9.7M | [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout.pdparams) | [PubLayNet dict](../../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt) |
-| ppyolov2_r50vd_dcn_365e_publaynet | The layout analysis English model trained on the PubLayNet dataset based on PP-YOLOv2 | 221M | [inference_moel]](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) / [trained model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet_pretrained.pdparams) | sme as above |
+| ppyolov2_r50vd_dcn_365e_publaynet | The layout analysis English model trained on the PubLayNet dataset based on PP-YOLOv2 | 221M | [inference_moel](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) / [trained model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet_pretrained.pdparams) | same as above |
 | picodet_lcnet_x1_0_fgd_layout_cdla | The layout analysis Chinese model trained on the CDLA dataset, the model can recognition 10 types of areas such as **Table、Figure、Figure caption、Table、Table caption、Header、Footer、Reference、Equation** | 9.7M | [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla.pdparams) | [CDLA dict](../../ppocr/utils/dict/layout_dict/layout_cdla_dict.txt) |
 | picodet_lcnet_x1_0_fgd_layout_table | The layout analysis model trained on the table dataset, the model can detect tables in Chinese and English documents                     | 9.7M                                                  | [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_table_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_table.pdparams) | [Table dict](../../ppocr/utils/dict/layout_dict/layout_table_dict.txt) |
 | ppyolov2_r50vd_dcn_365e_tableBank_word | The layout analysis model trained on the TableBank Word dataset based on PP-YOLOv2, the model can detect  tables  in English documents | 221M | [inference model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) | same as above |

ppstructure/docs/quickstart.md

@@ -48,7 +48,7 @@
 pip3 install "paddleocr>=2.6"
 
 # 安装 图像方向分类依赖包paddleclas（如不需要图像方向分类功能，可跳过）
-pip3 install paddleclas
+pip3 install paddleclas>=2.4.3
 
 # 安装 关键信息抽取 依赖包（如不需要KIE功能，可跳过）
 pip3 install -r ppstructure/kie/requirements.txt

ppstructure/docs/quickstart_en.md

@@ -50,7 +50,7 @@ For more software version requirements, please refer to the instructions in [Ins
 pip3 install "paddleocr>=2.6"
 
 # Install the image direction classification dependency package paddleclas (if you do not use the image direction classification, you can skip it)
-pip3 install paddleclas
+pip3 install paddleclas>=2.4.3
 
 # Install the KIE dependency packages (if you do not use the KIE, you can skip it)
 pip3 install -r kie/requirements.txt

ppstructure/layout/README_ch.md

+简体中文 | [English](README.md)
+
+# 版面分析
+
 - [1. 简介](#1-简介)
 - [2. 安装](#2-安装)
   - [2.1 安装PaddlePaddle](#21-安装paddlepaddle)
@@ -15,8 +19,6 @@
   - [6.1 模型导出](#61-模型导出)
   - [6.2 模型推理](#62-模型推理)
 
-# 版面分析
-
 ## 1. 简介
 
 版面分析指的是对图片形式的文档进行区域划分，定位其中的关键区域，如文字、标题、表格、图片等。版面分析算法基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)的轻量模型PP-PicoDet进行开发。
@@ -37,10 +39,10 @@
 python3 -m pip install --upgrade pip
 
 # GPU安装
-python3 -m pip install "paddlepaddle-gpu>=2.2" -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install "paddlepaddle-gpu>=2.3" -i https://mirror.baidu.com/pypi/simple
 
 # CPU安装
-python3 -m pip install "paddlepaddle>=2.2" -i https://mirror.baidu.com/pypi/simple
+python3 -m pip install "paddlepaddle>=2.3" -i https://mirror.baidu.com/pypi/simple
 ```
 更多需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
 

ppstructure/recovery/README.md

@@ -66,7 +66,7 @@ git clone https://gitee.com/paddlepaddle/PaddleOCR
 
 - **(2) Install recovery's `requirements`**
 
-The layout restoration is exported as docx and PDF files, so python-docx and docx2pdf API need to be installed, and fitz and PyMuPDF apis need to be installed to process the input files in pdf format.
+The layout restoration is exported as docx and PDF files, so python-docx and docx2pdf API need to be installed, and PyMuPDF api([requires Python >= 3.7](https://pypi.org/project/PyMuPDF/)) need to be installed to process the input files in pdf format.
 
 ```bash
 python3 -m pip install -r ppstructure/recovery/requirements.txt

ppstructure/recovery/README_ch.md

@@ -68,7 +68,7 @@ git clone https://gitee.com/paddlepaddle/PaddleOCR
 
 - **（2）安装recovery的`requirements`**
 
-版面恢复导出为docx、pdf文件，所以需要安装python-docx、docx2pdf API，同时处理pdf格式的输入文件，需要安装fitz、PyMuPDF API。
+版面恢复导出为docx、pdf文件，所以需要安装python-docx、docx2pdf API，同时处理pdf格式的输入文件，需要安装PyMuPDF API([要求Python >= 3.7](https://pypi.org/project/PyMuPDF/))。
 
 ```bash
 python3 -m pip install -r ppstructure/recovery/requirements.txt

ppstructure/recovery/requirements.txt

 python-docx
 docx2pdf
-fitz
-PyMuPDF==1.16.14
+PyMuPDF
 beautifulsoup4
\ No newline at end of file