add vqa_ser to ppstructure predict pipeline

ppstructure/README.md

@@ -153,7 +153,7 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_in
 wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
 cd ..
 
-python3 predict_system.py --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=ch --output=../output/table --vis_font_path=../doc/fonts/simfang.ttf
+python3 predict_system.py --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --output=../output/table --vis_font_path=../doc/fonts/simfang.ttf
 ```
 After running, each image will have a directory with the same name under the directory specified in the output field. Each table in the picture will be stored as an excel and figure area will be cropped and saved, the excel and image file name will be the coordinates of the table in the image.
 

ppstructure/README_ch.md

@@ -8,11 +8,37 @@ PP-Structure是一个可用于复杂文档结构分析和处理的OCR工具包
 - 支持表格区域进行结构化分析，最终结果输出Excel文件
 - 支持python whl包和命令行两种方式，简单易用
 - 支持版面分析和表格结构化两类任务自定义训练
+- 支持文档关键信息提取-SER和RE任务
+
 
 ## 1. 效果展示
 
+### 1.1 版面分析和表格识别
+
 <img src="../doc/table/ppstructure.GIF" width="100%"/>
 
+### 1.2 VQA
+
+* SER
+
+![](./vqa/images/result_ser/zh_val_0_ser.jpg) | ![](./vqa/images/result_ser/zh_val_42_ser.jpg)
+---|---
+
+图中不同颜色的框表示不同的类别，对于XFUN数据集，有`QUESTION`, `ANSWER`, `HEADER` 3种类别
+
+* 深紫色：HEADER
+* 浅紫色：QUESTION
+* 军绿色：ANSWER
+
+在OCR检测框的左上方也标出了对应的类别和OCR识别结果。
+
+* RE
+
+![](./vqa/images/result_re/zh_val_21_re.jpg) | ![](./vqa/images/result_re/zh_val_40_re.jpg)
+---|---
+
+
+图中红色框表示问题，蓝色框表示答案，问题和答案之间使用绿色线连接。在OCR检测框的左上方也标出了对应的类别和OCR识别结果。
 
 
 ## 2. 安装
@@ -33,10 +59,16 @@ python3 -m pip install paddlepaddle-gpu==2.1.1 -i https://mirror.baidu.com/pypi/
 ```
 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。
 
-- **(2) 安装 Layout-Parser**
+- **(2) 安装依赖 **
 
 ```bash
+# 版面分析所需  Layout-Parser
 pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
+
+# VQA所需 PaddleNLP
+git clone https://github.com/PaddlePaddle/PaddleNLP -b develop
+cd PaddleNLP
+pip3 install -e .
 ```
 
 ### 2.2 安装PaddleOCR（包含PP-OCR和PP-Structure）
@@ -44,7 +76,7 @@ pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-
 - **（1) PIP快速安装PaddleOCR whl包（仅预测）**
 
 ```bash
-pip install "paddleocr>=2.2" # 推荐使用2.2+版本
+pip3 install "paddleocr>=2.2" # 推荐使用2.2+版本
 ```
 
 - **（2) 完整克隆PaddleOCR源码（预测+训练）**
@@ -63,12 +95,14 @@ git clone https://gitee.com/paddlepaddle/PaddleOCR
 
 ### 3.1 命令行使用（默认参数，极简）
 
+* 版面分析+表格识别
 ```bash
 paddleocr --image_dir=../doc/table/1.png --type=structure
 ```
 
 ### 3.2 Python脚本使用（自定义参数，灵活）
 
+* 版面分析+表格识别
 ```python
 import os
 import cv2
@@ -98,6 +132,7 @@ im_show.save('result.jpg')
 ### 3.3 返回结果说明
 PP-Structure的返回结果为一个dict组成的list，示例如下
 
+* 版面分析+表格识别
 ```shell
 [
   {   'type': 'Text',
@@ -130,7 +165,7 @@ dict 里各个字段说明如下
 运行完成后，每张图片会在`output`字段指定的目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名名为表格在图片里的坐标。
 
 
-## 4. PP-Structure Pipeline介绍
+## 4. PP-Structure 版面分析+表格识别 Pipeline介绍
 
 ![pipeline](../doc/table/pipeline.jpg)
 
@@ -148,6 +183,8 @@ dict 里各个字段说明如下
 
 使用如下命令即可完成预测引擎的推理
 
+* 版面分析+表格识别
+
 ```python
 cd ppstructure
 
@@ -161,9 +198,24 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_in
 wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
 cd ..
 
-python3 predict_system.py --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=ch --output=../output/table --vis_font_path=../doc/fonts/simfang.ttf
+python3 predict_system.py --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --output=../output/table --vis_font_path=../doc/fonts/simfang.ttf
 ```
-运行完成后，每张图片会在`output`字段指定的目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名名为表格在图片里的坐标。
+运行完成后，每张图片会在`output`字段指定的目录下的`talbe`目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名名为表格在图片里的坐标。
+
+* VQA
+
+```python
+cd ppstructure
+
+# 下载模型
+mkdir inference && cd inference
+# 下载SER xfun 模型并解压
+wget https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar && tar xf PP-Layout_v1.0_ser_pretrained.tar
+cd ..
+
+python3 predict_system.py --model_name_or_path=vqa/PP-Layout_v1.0_ser_pretrained/ --mode=vqa --image_dir=vqa/images/input/zh_val_0.jpg  --vis_font_path=../doc/fonts/simfang.ttf
+```
+运行完成后，每张图片会在`output`字段指定的目录下的`vqa`目录下存放可视化之后的图片，图片名和输入图片名一致。
 
 **Model List**
 
@@ -185,4 +237,11 @@ OCR和表格识别模型
 |en_ppocr_mobile_v2.0_table_rec|PubLayNet数据集训练的英文表格场景的文字识别|6.9M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_rec_train.tar) |
 |en_ppocr_mobile_v2.0_table_structure|PubLayNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) |
 
+VQA
+
+|模型名称|模型简介|推理模型大小|下载地址|
+| --- | --- | --- | --- |
+|PP-Layout_v1.0_ser_pretrained|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar) |
+|PP-Layout_v1.0_re_pretrained|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar) |
+
 如需要使用其他模型，可以在 [model_list](../doc/doc_ch/models_list.md) 下载模型或者使用自己训练好的模型配置到`det_model_dir`,`rec_model_dir`,`table_model_dir`三个字段即可。

ppstructure/predict_system.py

@@ -30,6 +30,7 @@ from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from ppocr.utils.logging import get_logger
 from tools.infer.predict_system import TextSystem
 from ppstructure.table.predict_table import TableSystem, to_excel
+from ppstructure.vqa.infer_ser_e2e import SerPredictor, draw_ser_results
 from ppstructure.utility import parse_args, draw_structure_result
 
 logger = get_logger()
@@ -37,53 +38,75 @@ logger = get_logger()
 
 class OCRSystem(object):
     def __init__(self, args):
-        import layoutparser as lp
+        self.mode = args.mode
-        # args.det_limit_type = 'resize_long'
+        if self.mode == 'structure':
-        args.drop_score = 0
+            import layoutparser as lp
-        if not args.show_log:
+            # args.det_limit_type = 'resize_long'
-            logger.setLevel(logging.INFO)
+            args.drop_score = 0
-        self.text_system = TextSystem(args)
+            if not args.show_log:
-        self.table_system = TableSystem(args, self.text_system.text_detector, self.text_system.text_recognizer)
+                logger.setLevel(logging.INFO)
+            self.text_system = TextSystem(args)
+            self.table_system = TableSystem(args,
+                                            self.text_system.text_detector,
+                                            self.text_system.text_recognizer)
 
-        config_path = None
+            config_path = None
-        model_path = None
+            model_path = None
-        if os.path.isdir(args.layout_path_model):
+            if os.path.isdir(args.layout_path_model):
-            model_path = args.layout_path_model
+                model_path = args.layout_path_model
-        else:
+            else:
-            config_path = args.layout_path_model
+                config_path = args.layout_path_model
-        self.table_layout = lp.PaddleDetectionLayoutModel(config_path=config_path,
+            self.table_layout = lp.PaddleDetectionLayoutModel(
-                                                          model_path=model_path,
+                config_path=config_path,
-                                                          threshold=0.5, enable_mkldnn=args.enable_mkldnn,
+                model_path=model_path,
-                                                          enforce_cpu=not args.use_gpu, thread_num=args.cpu_threads)
+                threshold=0.5,
-        self.use_angle_cls = args.use_angle_cls
+                enable_mkldnn=args.enable_mkldnn,
-        self.drop_score = args.drop_score
+                enforce_cpu=not args.use_gpu,
+                thread_num=args.cpu_threads)
+            self.use_angle_cls = args.use_angle_cls
+            self.drop_score = args.drop_score
+        elif self.mode == 'vqa':
+            self.vqa_engine = SerPredictor(args)
 
     def __call__(self, img):
-        ori_im = img.copy()
+        if self.mode == 'structure':
-        layout_res = self.table_layout.detect(img[..., ::-1])
+            ori_im = img.copy()
-        res_list = []
+            layout_res = self.table_layout.detect(img[..., ::-1])
-        for region in layout_res:
+            res_list = []
-            x1, y1, x2, y2 = region.coordinates
+            for region in layout_res:
-            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+                x1, y1, x2, y2 = region.coordinates
-            roi_img = ori_im[y1:y2, x1:x2, :]
+                x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
-            if region.type == 'Table':
+                roi_img = ori_im[y1:y2, x1:x2, :]
-                res = self.table_system(roi_img)
+                if region.type == 'Table':
-            else:
+                    res = self.table_system(roi_img)
-                filter_boxes, filter_rec_res = self.text_system(roi_img)
+                else:
-                filter_boxes = [x + [x1, y1] for x in filter_boxes]
+                    filter_boxes, filter_rec_res = self.text_system(roi_img)
-                filter_boxes = [x.reshape(-1).tolist() for x in filter_boxes]
+                    filter_boxes = [x + [x1, y1] for x in filter_boxes]
-                # remove style char
+                    filter_boxes = [
-                style_token = ['<strike>', '<strike>', '<sup>', '</sub>', '<b>', '</b>', '<sub>', '</sup>',
+                        x.reshape(-1).tolist() for x in filter_boxes
-                               '<overline>', '</overline>', '<underline>', '</underline>', '<i>', '</i>']
+                    ]
-                filter_rec_res_tmp = []
+                    # remove style char
-                for rec_res in filter_rec_res:
+                    style_token = [
-                    rec_str, rec_conf = rec_res
+                        '<strike>', '<strike>', '<sup>', '</sub>', '<b>',
-                    for token in style_token:
+                        '</b>', '<sub>', '</sup>', '<overline>', '</overline>',
-                        if token in rec_str:
+                        '<underline>', '</underline>', '<i>', '</i>'
-                            rec_str = rec_str.replace(token, '')
+                    ]
-                    filter_rec_res_tmp.append((rec_str, rec_conf))
+                    filter_rec_res_tmp = []
-                res = (filter_boxes, filter_rec_res_tmp)
+                    for rec_res in filter_rec_res:
-            res_list.append({'type': region.type, 'bbox': [x1, y1, x2, y2], 'img': roi_img, 'res': res})
+                        rec_str, rec_conf = rec_res
+                        for token in style_token:
+                            if token in rec_str:
+                                rec_str = rec_str.replace(token, '')
+                        filter_rec_res_tmp.append((rec_str, rec_conf))
+                    res = (filter_boxes, filter_rec_res_tmp)
+                res_list.append({
+                    'type': region.type,
+                    'bbox': [x1, y1, x2, y2],
+                    'img': roi_img,
+                    'res': res
+                })
+        elif self.mode == 'vqa':
+            res_list, _ = self.vqa_engine(img)
         return res_list
 
 
@@ -91,29 +114,35 @@ def save_structure_res(res, save_folder, img_name):
     excel_save_folder = os.path.join(save_folder, img_name)
     os.makedirs(excel_save_folder, exist_ok=True)
     # save res
-    with open(os.path.join(excel_save_folder, 'res.txt'), 'w', encoding='utf8') as f:
+    with open(
+            os.path.join(excel_save_folder, 'res.txt'), 'w',
+            encoding='utf8') as f:
         for region in res:
             if region['type'] == 'Table':
-                excel_path = os.path.join(excel_save_folder, '{}.xlsx'.format(region['bbox']))
+                excel_path = os.path.join(excel_save_folder,
+                                          '{}.xlsx'.format(region['bbox']))
                 to_excel(region['res'], excel_path)
             if region['type'] == 'Figure':
                 roi_img = region['img']
-                img_path = os.path.join(excel_save_folder, '{}.jpg'.format(region['bbox']))
+                img_path = os.path.join(excel_save_folder,
+                                        '{}.jpg'.format(region['bbox']))
                 cv2.imwrite(img_path, roi_img)
             else:
                 for box, rec_res in zip(region['res'][0], region['res'][1]):
-                    f.write('{}\t{}\n'.format(np.array(box).reshape(-1).tolist(), rec_res))
+                    f.write('{}\t{}\n'.format(
+                        np.array(box).reshape(-1).tolist(), rec_res))
 
 
 def main(args):
     image_file_list = get_image_file_list(args.image_dir)
     image_file_list = image_file_list
     image_file_list = image_file_list[args.process_id::args.total_process_num]
-    save_folder = args.output
-    os.makedirs(save_folder, exist_ok=True)
 
     structure_sys = OCRSystem(args)
     img_num = len(image_file_list)
+    save_folder = os.path.join(args.output, structure_sys.mode)
+    os.makedirs(save_folder, exist_ok=True)
+
     for i, image_file in enumerate(image_file_list):
         logger.info("[{}/{}] {}".format(i, img_num, image_file))
         img, flag = check_and_read_gif(image_file)
@@ -126,10 +155,16 @@ def main(args):
             continue
         starttime = time.time()
         res = structure_sys(img)
-        save_structure_res(res, save_folder, img_name)
+
-        draw_img = draw_structure_result(img, res, args.vis_font_path)
+        if structure_sys.mode == 'structure':
-        cv2.imwrite(os.path.join(save_folder, img_name, 'show.jpg'), draw_img)
+            save_structure_res(res, save_folder, img_name)
-        logger.info('result save to {}'.format(os.path.join(save_folder, img_name)))
+            draw_img = draw_structure_result(img, res, args.vis_font_path)
+            img_save_path = os.path.join(save_folder, img_name, 'show.jpg')
+        elif structure_sys.mode == 'vqa':
+            draw_img = draw_ser_results(img, res, args.vis_font_path)
+            img_save_path = os.path.join(save_folder, img_name + '.jpg')
+        cv2.imwrite(img_save_path, draw_img)
+        logger.info('result save to {}'.format(img_save_path))
         elapse = time.time() - starttime
         logger.info("Predict time : {:.3f}s".format(elapse))
 

ppstructure/table/README.md

@@ -20,9 +20,9 @@ We evaluated the algorithm on the PubTabNet<sup>[1]</sup> eval dataset, and the
 
 
 |Method|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|
-| --- | --- | 
+| --- | --- |
-| EDD<sup>[2]</sup> | 88.3 | 
+| EDD<sup>[2]</sup> | 88.3 |
-| Ours | 93.32 | 
+| Ours | 93.32 |
 
 ## 3. How to use
 
@@ -41,7 +41,7 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_tab
 wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
 cd ..
 # run
-python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table
+python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_dict_path=../ppocr/utils/dict/en_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table
 ```
 Note: The above model is trained on the PubLayNet dataset and only supports English scanning scenarios. If you need to identify other scenarios, you need to train the model yourself and replace the three fields `det_model_dir`, `rec_model_dir`, `table_model_dir`.
 
@@ -82,8 +82,8 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo
 The table uses [TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src) as the evaluation metric of the model. Before the model evaluation, the three models in the pipeline need to be exported as inference models (we have provided them), and the gt for evaluation needs to be prepared. Examples of gt are as follows:
 ```json
 {"PMC4289340_004_00.png": [
-  ["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>",  "</tbody>", "</table>", "</body>", "</html>"], 
+  ["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>",  "</tbody>", "</table>", "</body>", "</html>"],
-  [[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]], 
+  [[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]],
   [["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]
 ]}
 ```
@@ -95,7 +95,7 @@ In gt json, the key is the image name, the value is the corresponding gt, and gt
 Use the following command to evaluate. After the evaluation is completed, the teds indicator will be output.
 ```python
 cd PaddleOCR/ppstructure
-python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
+python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
 ```
 
 If the PubLatNet eval dataset is used, it will be output
@@ -113,4 +113,4 @@ After running, the excel sheet of each picture will be saved in the directory sp
 
 Reference
 1. https://github.com/ibm-aur-nlp/PubTabNet
-2. https://arxiv.org/pdf/1911.10683
+2. https://arxiv.org/pdf/1911.10683

ppstructure/table/README_ch.md

@@ -34,9 +34,9 @@
 
 
 |算法|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|
-| --- | --- | 
+| --- | --- |
-| EDD<sup>[2]</sup> | 88.3 | 
+| EDD<sup>[2]</sup> | 88.3 |
-| Ours | 93.32 | 
+| Ours | 93.32 |
 
 <a name="3"></a>
 ## 3. 使用
@@ -56,7 +56,7 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_tab
 wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
 cd ..
 # 执行预测
-python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table
+python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=inference/en_ppocr_mobile_v2.0_table_rec_infer --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_dict_path=../ppocr/utils/dict/en_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table
 ```
 运行完成后，每张图片的excel表格会保存到output字段指定的目录下
 
@@ -94,8 +94,8 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo
 表格使用 [TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src) 作为模型的评估指标。在进行模型评估之前，需要将pipeline中的三个模型分别导出为inference模型(我们已经提供好)，还需要准备评估的gt， gt示例如下:
 ```json
 {"PMC4289340_004_00.png": [
-  ["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>",  "</tbody>", "</table>", "</body>", "</html>"], 
+  ["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>",  "</tbody>", "</table>", "</body>", "</html>"],
-  [[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]], 
+  [[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]],
   [["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]
 ]}
 ```
@@ -107,7 +107,7 @@ json 中，key为图片名，value为对应的gt，gt是一个由三个item组
 准备完成后使用如下命令进行评估，评估完成后会输出teds指标。
 ```python
 cd PaddleOCR/ppstructure
-python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
+python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
 ```
 如使用PubLatNet评估数据集，将会输出
 ```bash
@@ -123,4 +123,4 @@ python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model
 
 Reference
 1. https://github.com/ibm-aur-nlp/PubTabNet
-2. https://arxiv.org/pdf/1911.10683
+2. https://arxiv.org/pdf/1911.10683

ppstructure/utility.py

@@ -21,13 +21,31 @@ def init_args():
     parser = infer_args()
 
     # params for output
-    parser.add_argument("--output", type=str, default='./output/table')
+    parser.add_argument("--output", type=str, default='./output')
     # params for table structure
     parser.add_argument("--table_max_len", type=int, default=488)
     parser.add_argument("--table_model_dir", type=str)
     parser.add_argument("--table_char_type", type=str, default='en')
-    parser.add_argument("--table_char_dict_path", type=str, default="../ppocr/utils/dict/table_structure_dict.txt")
+    parser.add_argument(
-    parser.add_argument("--layout_path_model", type=str, default="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config")
+        "--table_char_dict_path",
+        type=str,
+        default="../ppocr/utils/dict/table_structure_dict.txt")
+    parser.add_argument(
+        "--layout_path_model",
+        type=str,
+        default="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config")
+
+    # params for ser
+    parser.add_argument("--model_name_or_path", type=str)
+    parser.add_argument("--max_seq_length", type=int, default=512)
+    parser.add_argument(
+        "--label_map_path", type=str, default='./vqa/labels/labels_ser.txt')
+
+    parser.add_argument(
+        "--mode",
+        type=str,
+        default='structure',
+        help='structure and vqa is supported')
     return parser
 
 
@@ -48,5 +66,6 @@ def draw_structure_result(image, result, font_path):
                 boxes.append(np.array(box).reshape(-1, 2))
                 txts.append(rec_res[0])
                 scores.append(rec_res[1])
-    im_show = draw_ocr_box_txt(image, boxes, txts, scores, font_path=font_path,drop_score=0)
+    im_show = draw_ocr_box_txt(
-    return im_show
+        image, boxes, txts, scores, font_path=font_path, drop_score=0)
+    return im_show

ppstructure/vqa/infer_ser_e2e.py

@@ -23,12 +23,10 @@ from PIL import Image
 import paddle
 from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer, LayoutXLMForTokenClassification
 
-from paddleocr import PaddleOCR
-
 # relative reference
-from utils import parse_args, get_image_file_list, draw_ser_results, get_bio_label_maps
+from .utils import parse_args, get_image_file_list, draw_ser_results, get_bio_label_maps
 
-from utils import pad_sentences, split_page, preprocess, postprocess, merge_preds_list_with_ocr_info
+from .utils import pad_sentences, split_page, preprocess, postprocess, merge_preds_list_with_ocr_info
 
 
 def trans_poly_to_bbox(poly):
@@ -52,6 +50,7 @@ def parse_ocr_info_for_ser(ocr_result):
 
 class SerPredictor(object):
     def __init__(self, args):
+
         self.max_seq_length = args.max_seq_length
 
         # init ser token and model
@@ -62,9 +61,11 @@ class SerPredictor(object):
         self.model.eval()
 
         # init ocr_engine
+        from paddleocr import PaddleOCR
+
         self.ocr_engine = PaddleOCR(
-            rec_model_dir=args.ocr_rec_model_dir,
+            rec_model_dir=args.rec_model_dir,
-            det_model_dir=args.ocr_det_model_dir,
+            det_model_dir=args.det_model_dir,
             use_angle_cls=False,
             show_log=False)
         # init dict

ppstructure/vqa/utils.py

@@ -380,8 +380,8 @@ def parse_args():
     parser.add_argument("--seed", type=int, default=2048,
                         help="random seed for initialization",)
 
-    parser.add_argument("--ocr_rec_model_dir", default=None, type=str, )
+    parser.add_argument("--rec_model_dir", default=None, type=str, )
-    parser.add_argument("--ocr_det_model_dir", default=None, type=str, )
+    parser.add_argument("--det_model_dir", default=None, type=str, )
     parser.add_argument(
         "--label_map_path", default="./labels/labels_ser.txt", type=str, required=False, )
     parser.add_argument("--infer_imgs", default=None, type=str, required=False)