Merge pull request #5467 from WenmuZhou/vqa

[PP-Structure] add LayoutLMv2 to DOC-VQA

configs/vqa/re/layoutlmv2.yml

+Global:
+  use_gpu: True
+  epoch_num: &epoch_num 200
+  log_smooth_window: 10
+  print_batch_step: 10
+  save_model_dir: ./output/re_layoutlmv2/
+  save_epoch_step: 2000
+  # evaluation is run every 10 iterations after the 0th iteration
+  eval_batch_step: [ 0, 19 ]
+  cal_metric_during_train: False
+  save_inference_dir:
+  use_visualdl: False
+  seed: 2048
+  infer_img: doc/vqa/input/zh_val_21.jpg
+  save_res_path: ./output/re/
+
+Architecture:
+  model_type: vqa
+  algorithm: &algorithm "LayoutLMv2"
+  Transform:
+  Backbone:
+    name: LayoutLMv2ForRe
+    pretrained: True
+    checkpoints: 
+
+Loss:
+  name: LossFromOutput
+  key: loss
+  reduction: mean
+
+Optimizer:
+  name: AdamW
+  beta1: 0.9
+  beta2: 0.999
+  clip_norm: 10
+  lr:
+    learning_rate: 0.00005
+    warmup_epoch: 10
+  regularizer:
+    name: L2
+    factor: 0.00000
+    
+PostProcess:
+  name: VQAReTokenLayoutLMPostProcess
+
+Metric:
+  name: VQAReTokenMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/XFUND/zh_train/image
+    label_file_list: 
+      - train_data/XFUND/zh_train/xfun_normalize_train.json
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: RGB
+          channel_first: False
+      - VQATokenLabelEncode: # Class handling label
+          contains_re: True
+          algorithm: *algorithm
+          class_path: &class_path ppstructure/vqa/labels/labels_ser.txt
+      - VQATokenPad:
+          max_seq_len: &max_seq_len 512
+          return_attention_mask: True
+      - VQAReTokenRelation:
+      - VQAReTokenChunk:
+          max_seq_len: *max_seq_len
+      - Resize:
+          size: [224,224]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 8
+    collate_fn: ListCollator
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/XFUND/zh_val/image
+    label_file_list:
+      - train_data/XFUND/zh_val/xfun_normalize_val.json
+    transforms:
+      - DecodeImage: # load image
+          img_mode: RGB
+          channel_first: False
+      - VQATokenLabelEncode: # Class handling label
+          contains_re: True
+          algorithm: *algorithm
+          class_path: *class_path
+      - VQATokenPad:
+          max_seq_len: *max_seq_len
+          return_attention_mask: True
+      - VQAReTokenRelation:
+      - VQAReTokenChunk:
+          max_seq_len: *max_seq_len
+      - Resize:
+          size: [224,224]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'input_ids', 'bbox', 'image', 'attention_mask', 'token_type_ids','entities', 'relations'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 8
+    collate_fn: ListCollator

configs/vqa/re/layoutxlm.yml

@@ -35,6 +35,7 @@ Optimizer:
   clip_norm: 10
   lr:
     learning_rate: 0.00005
+    warmup_epoch: 10
   regularizer:
     name: L2
     factor: 0.00000
@@ -81,7 +82,7 @@ Train:
     shuffle: True
     drop_last: False
     batch_size_per_card: 8
-    num_workers: 4
+    num_workers: 8
     collate_fn: ListCollator
 
 Eval:
@@ -118,5 +119,5 @@ Eval:
     shuffle: False
     drop_last: False
     batch_size_per_card: 8
-    num_workers: 4
+    num_workers: 8
     collate_fn: ListCollator

configs/vqa/ser/layoutlmv2.yml

+Global:
+  use_gpu: True
+  epoch_num: &epoch_num 200
+  log_smooth_window: 10
+  print_batch_step: 10
+  save_model_dir: ./output/ser_layoutlmv2/
+  save_epoch_step: 2000
+  # evaluation is run every 10 iterations after the 0th iteration
+  eval_batch_step: [ 0, 19 ]
+  cal_metric_during_train: False
+  save_inference_dir:
+  use_visualdl: False
+  seed: 2022
+  infer_img: doc/vqa/input/zh_val_0.jpg
+  save_res_path: ./output/ser/
+
+Architecture:
+  model_type: vqa
+  algorithm: &algorithm "LayoutLMv2"
+  Transform:
+  Backbone:
+    name: LayoutLMv2ForSer
+    pretrained: True
+    checkpoints:
+    num_classes: &num_classes 7
+
+Loss:
+  name: VQASerTokenLayoutLMLoss
+  num_classes: *num_classes
+
+Optimizer:
+  name: AdamW
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Linear
+    learning_rate: 0.00005
+    epochs: *epoch_num
+    warmup_epoch: 2
+  regularizer:
+  
+    name: L2
+    factor: 0.00000
+    
+PostProcess:
+  name: VQASerTokenLayoutLMPostProcess
+  class_path: &class_path ppstructure/vqa/labels/labels_ser.txt
+
+Metric:
+  name: VQASerTokenMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/XFUND/zh_train/image
+    label_file_list: 
+      - train_data/XFUND/zh_train/xfun_normalize_train.json
+    transforms:
+      - DecodeImage: # load image
+          img_mode: RGB
+          channel_first: False
+      - VQATokenLabelEncode: # Class handling label
+          contains_re: False
+          algorithm: *algorithm
+          class_path: *class_path
+      - VQATokenPad:
+          max_seq_len: &max_seq_len 512
+          return_attention_mask: True
+      - VQASerTokenChunk:
+          max_seq_len: *max_seq_len
+      - Resize:
+          size: [224,224]
+      - NormalizeImage:
+          scale: 1
+          mean: [ 123.675, 116.28, 103.53 ]
+          std: [ 58.395, 57.12, 57.375 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'input_ids','labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/XFUND/zh_val/image
+    label_file_list:
+      - train_data/XFUND/zh_val/xfun_normalize_val.json
+    transforms:
+      - DecodeImage: # load image
+          img_mode: RGB
+          channel_first: False
+      - VQATokenLabelEncode: # Class handling label
+          contains_re: False
+          algorithm: *algorithm
+          class_path: *class_path
+      - VQATokenPad:
+          max_seq_len: *max_seq_len
+          return_attention_mask: True
+      - VQASerTokenChunk:
+          max_seq_len: *max_seq_len
+      - Resize:
+          size: [224,224]
+      - NormalizeImage:
+          scale: 1
+          mean: [ 123.675, 116.28, 103.53 ]
+          std: [ 58.395, 57.12, 57.375 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'input_ids', 'labels', 'bbox', 'image', 'attention_mask', 'token_type_ids'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 4

ppocr/data/imaug/label_ops.py

@@ -799,7 +799,7 @@ class VQATokenLabelEncode(object):
                  ocr_engine=None,
                  **kwargs):
         super(VQATokenLabelEncode, self).__init__()
-        from paddlenlp.transformers import LayoutXLMTokenizer, LayoutLMTokenizer
+        from paddlenlp.transformers import LayoutXLMTokenizer, LayoutLMTokenizer, LayoutLMv2Tokenizer
         from ppocr.utils.utility import load_vqa_bio_label_maps
         tokenizer_dict = {
             'LayoutXLM': {
@@ -809,6 +809,10 @@ class VQATokenLabelEncode(object):
             'LayoutLM': {
                 'class': LayoutLMTokenizer,
                 'pretrained_model': 'layoutlm-base-uncased'
+            },
+            'LayoutLMv2': {
+                'class': LayoutLMv2Tokenizer,
+                'pretrained_model': 'layoutlmv2-base-uncased'
             }
         }
         self.contains_re = contains_re

ppocr/data/imaug/vqa/token/vqa_token_chunk.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from collections import defaultdict
+
 
 class VQASerTokenChunk(object):
     def __init__(self, max_seq_len=512, infer_mode=False, **kwargs):
@@ -39,6 +41,8 @@ class VQASerTokenChunk(object):
                     encoded_inputs_example[key] = data[key]
 
             encoded_inputs_all.append(encoded_inputs_example)
+        if len(encoded_inputs_all) == 0:
+            return None
         return encoded_inputs_all[0]
 
 
@@ -101,17 +105,18 @@ class VQAReTokenChunk(object):
                 "entities": self.reformat(entities_in_this_span),
                 "relations": self.reformat(relations_in_this_span),
             })
+            if len(item['entities']) > 0:
                 item['entities']['label'] = [
                     self.entities_labels[x] for x in item['entities']['label']
                 ]
                 encoded_inputs_all.append(item)
+        if len(encoded_inputs_all) == 0:
+            return None
         return encoded_inputs_all[0]
 
     def reformat(self, data):
-        new_data = {}
+        new_data = defaultdict(list)
         for item in data:
             for k, v in item.items():
-                if k not in new_data:
-                    new_data[k] = []
                 new_data[k].append(v)
         return new_data

ppocr/modeling/backbones/__init__.py

@@ -45,8 +45,11 @@ def build_backbone(config, model_type):
         from .table_mobilenet_v3 import MobileNetV3
         support_dict = ["ResNet", "MobileNetV3"]
     elif model_type == 'vqa':
-        from .vqa_layoutlm import LayoutLMForSer, LayoutXLMForSer, LayoutXLMForRe
-        support_dict = ["LayoutLMForSer", "LayoutXLMForSer", 'LayoutXLMForRe']
+        from .vqa_layoutlm import LayoutLMForSer, LayoutLMv2ForSer, LayoutLMv2ForRe, LayoutXLMForSer, LayoutXLMForRe
+        support_dict = [
+            "LayoutLMForSer", "LayoutLMv2ForSer", 'LayoutLMv2ForRe',
+            "LayoutXLMForSer", 'LayoutXLMForRe'
+        ]
     else:
         raise NotImplementedError
 

ppocr/modeling/backbones/vqa_layoutlm.py

@@ -21,12 +21,14 @@ from paddle import nn
 
 from paddlenlp.transformers import LayoutXLMModel, LayoutXLMForTokenClassification, LayoutXLMForRelationExtraction
 from paddlenlp.transformers import LayoutLMModel, LayoutLMForTokenClassification
+from paddlenlp.transformers import LayoutLMv2Model, LayoutLMv2ForTokenClassification, LayoutLMv2ForRelationExtraction
 
 __all__ = ["LayoutXLMForSer", 'LayoutLMForSer']
 
 pretrained_model_dict = {
     LayoutXLMModel: 'layoutxlm-base-uncased',
-    LayoutLMModel: 'layoutlm-base-uncased'
+    LayoutLMModel: 'layoutlm-base-uncased',
+    LayoutLMv2Model: 'layoutlmv2-base-uncased'
 }
 
 
@@ -58,12 +60,34 @@ class NLPBaseModel(nn.Layer):
         self.out_channels = 1
 
 
-class LayoutXLMForSer(NLPBaseModel):
+class LayoutLMForSer(NLPBaseModel):
     def __init__(self, num_classes, pretrained=True, checkpoints=None,
                  **kwargs):
-        super(LayoutXLMForSer, self).__init__(
-            LayoutXLMModel,
-            LayoutXLMForTokenClassification,
+        super(LayoutLMForSer, self).__init__(
+            LayoutLMModel,
+            LayoutLMForTokenClassification,
+            'ser',
+            pretrained,
+            checkpoints,
+            num_classes=num_classes)
+
+    def forward(self, x):
+        x = self.model(
+            input_ids=x[0],
+            bbox=x[2],
+            attention_mask=x[4],
+            token_type_ids=x[5],
+            position_ids=None,
+            output_hidden_states=False)
+        return x
+
+
+class LayoutLMv2ForSer(NLPBaseModel):
+    def __init__(self, num_classes, pretrained=True, checkpoints=None,
+                 **kwargs):
+        super(LayoutLMv2ForSer, self).__init__(
+            LayoutLMv2Model,
+            LayoutLMv2ForTokenClassification,
             'ser',
             pretrained,
             checkpoints,
@@ -82,12 +106,12 @@ class LayoutXLMForSer(NLPBaseModel):
         return x[0]
 
 
-class LayoutLMForSer(NLPBaseModel):
+class LayoutXLMForSer(NLPBaseModel):
     def __init__(self, num_classes, pretrained=True, checkpoints=None,
                  **kwargs):
-        super(LayoutLMForSer, self).__init__(
-            LayoutLMModel,
-            LayoutLMForTokenClassification,
+        super(LayoutXLMForSer, self).__init__(
+            LayoutXLMModel,
+            LayoutXLMForTokenClassification,
             'ser',
             pretrained,
             checkpoints,
@@ -97,10 +121,33 @@ class LayoutLMForSer(NLPBaseModel):
         x = self.model(
             input_ids=x[0],
             bbox=x[2],
+            image=x[3],
             attention_mask=x[4],
             token_type_ids=x[5],
             position_ids=None,
-            output_hidden_states=False)
+            head_mask=None,
+            labels=None)
+        return x[0]
+
+
+class LayoutLMv2ForRe(NLPBaseModel):
+    def __init__(self, pretrained=True, checkpoints=None, **kwargs):
+        super(LayoutLMv2ForRe, self).__init__(LayoutLMv2Model,
+                                              LayoutLMv2ForRelationExtraction,
+                                              're', pretrained, checkpoints)
+
+    def forward(self, x):
+        x = self.model(
+            input_ids=x[0],
+            bbox=x[1],
+            labels=None,
+            image=x[2],
+            attention_mask=x[3],
+            token_type_ids=x[4],
+            position_ids=None,
+            head_mask=None,
+            entities=x[5],
+            relations=x[6])
         return x
 
 

ppocr/optimizer/__init__.py

@@ -25,11 +25,8 @@ __all__ = ['build_optimizer']
 def build_lr_scheduler(lr_config, epochs, step_each_epoch):
     from . import learning_rate
     lr_config.update({'epochs': epochs, 'step_each_epoch': step_each_epoch})
-    if 'name' in lr_config:
-        lr_name = lr_config.pop('name')
+    lr_name = lr_config.pop('name', 'Const')
     lr = getattr(learning_rate, lr_name)(**lr_config)()
-    else:
-        lr = lr_config['learning_rate']
     return lr
 
 

ppocr/optimizer/learning_rate.py

@@ -276,3 +276,35 @@ class OneCycle(object):
                 end_lr=self.max_lr,
                 last_epoch=self.last_epoch)
         return learning_rate
+
+
+class Const(object):
+    """
+    Const learning rate decay
+    Args:
+        learning_rate(float): initial learning rate
+        step_each_epoch(int): steps each epoch
+        last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
+    """
+
+    def __init__(self,
+                 learning_rate,
+                 step_each_epoch,
+                 warmup_epoch=0,
+                 last_epoch=-1,
+                 **kwargs):
+        super(Const, self).__init__()
+        self.learning_rate = learning_rate
+        self.last_epoch = last_epoch
+        self.warmup_epoch = round(warmup_epoch * step_each_epoch)
+
+    def __call__(self):
+        learning_rate = self.learning_rate
+        if self.warmup_epoch > 0:
+            learning_rate = lr.LinearWarmup(
+                learning_rate=learning_rate,
+                warmup_steps=self.warmup_epoch,
+                start_lr=0.0,
+                end_lr=self.learning_rate,
+                last_epoch=self.last_epoch)
+        return learning_rate

ppstructure/README.md

@@ -13,20 +13,18 @@ English | [简体中文](README_ch.md)
     - [6.1.2 Table recognition](#612-table-recognition)
   - [6.2 DOC-VQA](#62-doc-vqa)
 - [7. Model List](#7-model-list)
-
-<a name="1"></a>
+  - [7.1 Layout analysis model](#71-layout-analysis-model)
+  - [7.2 OCR and table recognition model](#72-ocr-and-table-recognition-model)
+  - [7.3 DOC-VQA model](#73-doc-vqa-model)
 
 ## 1. Introduction
 
 PP-Structure is an OCR toolkit that can be used for document analysis and processing with complex structures, designed to help developers better complete document understanding tasks
 
-<a name="2"></a>
-
 ## 2. Update log
+* 2022.02.12 DOC-VQA add LayoutLMv2 model。
 * 2021.12.07 add [DOC-VQA SER and RE tasks](vqa/README.md)。
 
-<a name="3"></a>
-
 ## 3. Features
 
 The main features of PP-Structure are as follows:
@@ -38,21 +36,14 @@ The main features of PP-Structure are as follows:
 - Support custom training for layout analysis and table structure tasks
 - Support Document Visual Question Answering (DOC-VQA) tasks: Semantic Entity Recognition (SER) and Relation Extraction (RE)
 
-
-<a name="4"></a>
-
 ## 4. Results
 
-<a name="41"></a>
-
 ### 4.1 Layout analysis and table recognition
 
 <img src="../doc/table/ppstructure.GIF" width="100%"/>
 
 The figure shows the pipeline of layout analysis + table recognition. The image is first divided into four areas of image, text, title and table by layout analysis, and then OCR detection and recognition is performed on the three areas of image, text and title, and the table is performed table recognition, where the image will also be stored for use.
 
-<a name="42"></a>
-
 ### 4.2 DOC-VQA
 
 * SER
@@ -77,19 +68,12 @@ The corresponding category and OCR recognition results are also marked at the to
 
 In the figure, the red box represents the question, the blue box represents the answer, and the question and answer are connected by green lines. The corresponding category and OCR recognition results are also marked at the top left of the OCR detection box.
 
-
-<a name="5"></a>
-
 ## 5. Quick start
 
 Start from [Quick Installation](./docs/quickstart.md)
 
-<a name="6"></a>
-
 ## 6. PP-Structure System
 
-<a name="61"></a>
-
 ### 6.1 Layout analysis and table recognition
 
 ![pipeline](../doc/table/pipeline.jpg)
@@ -104,39 +88,33 @@ Layout analysis classifies image by region, including the use of Python scripts
 
 Table recognition converts table images into excel documents, which include the detection and recognition of table text and the prediction of table structure and cell coordinates. For detailed instructions, please refer to [document](table/README.md)
 
-<a name="62"></a>
-
 ### 6.2 DOC-VQA
 
 Document Visual Question Answering (DOC-VQA) if a type of Visual Question Answering (VQA), which includes Semantic Entity Recognition (SER) and Relation Extraction (RE) tasks. Based on SER task, text recognition and classification in images can be completed. Based on THE RE task, we can extract the relation of the text content in the image, such as judge the problem pair. For details, please refer to [document](vqa/README.md)
 
-
-<a name="7"></a>
-
 ## 7. Model List
 
-PP-Structure系列模型列表（更新中）
+PP-Structure Series Model List (Updating)
 
-* Layout analysis model
+### 7.1 Layout analysis model
 
 |model name|description|download|
 | --- | --- | --- |
 | ppyolov2_r50vd_dcn_365e_publaynet | The layout analysis model trained on the PubLayNet dataset can divide image into 5 types of areas **text, title, table, picture, and list** | [PubLayNet](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) |
 
-
-* OCR and table recognition model
+### 7.2 OCR and table recognition model
 
 |model name|description|model size|download|
 | --- | --- | --- | --- |
-|ch_ppocr_mobile_slim_v2.0_det|Slim pruned lightweight model, supporting Chinese, English, multilingual text detection|2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) |
-|ch_ppocr_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition|6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
-|en_ppocr_mobile_v2.0_table_structure|Table structure prediction of English table scene trained on PubLayNet dataset|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) |
+|ch_PP-OCRv2_det_slim|[New] Slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection| 3M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar)|
+|ch_PP-OCRv2_rec_slim|[New] Slim qunatization with distillation lightweight model, supporting Chinese, English, multilingual text recognition| 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) |
+|en_ppocr_mobile_v2.0_table_structure|Table structure prediction of English table scene trained on PubLayNet dataset| 18.6M |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) |
 
-* DOC-VQA model
+### 7.3 DOC-VQA model
 
 |model name|description|model size|download|
 | --- | --- | --- | --- |
-|PP-Layout_v1.0_ser_pretrained|SER model trained on xfun Chinese dataset based on LayoutXLM|1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar) |
-|PP-Layout_v1.0_re_pretrained|RE model trained on xfun Chinese dataset based on LayoutXLM|1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar) |
+|ser_LayoutXLM_xfun_zhd|SER model trained on xfun Chinese dataset based on LayoutXLM|1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
+|re_LayoutXLM_xfun_zh|RE model trained on xfun Chinese dataset based on LayoutXLM|1.4G|[inference model coming soon]() / [trained model](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
 
 If you need to use other models, you can download the model in [PPOCR model_list](../doc/doc_en/models_list_en.md) and  [PPStructure model_list](./docs/model_list.md)

ppstructure/README_ch.md

@@ -13,18 +13,17 @@
     - [6.1.2 表格识别](#612-表格识别)
   - [6.2 DOC-VQA](#62-doc-vqa)
 - [7. 模型库](#7-模型库)
+  - [7.1 版面分析模型](#71-版面分析模型)
+  - [7.2 OCR和表格识别模型](#72-ocr和表格识别模型)
+  - [7.2 DOC-VQA 模型](#72-doc-vqa-模型)
 
-<a name="1"></a>
 
 ## 1. 简介
 PP-Structure是一个可用于复杂文档结构分析和处理的OCR工具包，旨在帮助开发者更好的完成文档理解相关任务。
 
-<a name="2"></a>
-
 ## 2. 近期更新
-* 2021.12.07 新增DOC-[VQA任务SER和RE](vqa/README.md)。
-
-<a name="3"></a>
+* 2022.02.12 DOC-VQA增加LayoutLMv2模型。
+* 2021.12.07 新增[DOC-VQA任务SER和RE](vqa/README.md)。
 
 ## 3. 特性
 
@@ -36,22 +35,14 @@ PP-Structure的主要特性如下：
 - 支持版面分析和表格结构化两类任务自定义训练
 - 支持文档视觉问答(Document Visual Question Answering，DOC-VQA)任务-语义实体识别(Semantic Entity Recognition，SER)和关系抽取(Relation Extraction，RE)
 
-
-<a name="4"></a>
-
 ## 4. 效果展示
 
-<a name="41"></a>
-
 ### 4.1 版面分析和表格识别
 
 <img src="../doc/table/ppstructure.GIF" width="100%"/>
 
 图中展示了版面分析+表格识别的整体流程，图片先有版面分析划分为图像、文本、标题和表格四种区域，然后对图像、文本和标题三种区域进行OCR的检测识别，对表格进行表格识别，其中图像还会被存储下来以便使用。
 
-
-<a name="42"></a>
-
 ### 4.2 DOC-VQA
 
 * SER
@@ -75,18 +66,12 @@ PP-Structure的主要特性如下：
 
 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。
 
-<a name="5"></a>
-
 ## 5. 快速体验
 
 请参考[快速安装](./docs/quickstart.md)教程。
 
-<a name="6"></a>
-
 ## 6. PP-Structure 介绍
 
-<a name="61"></a>
-
 ### 6.1 版面分析+表格识别
 
 ![pipeline](../doc/table/pipeline.jpg)
@@ -101,39 +86,34 @@ PP-Structure的主要特性如下：
 
 表格识别将表格图片转换为excel文档，其中包含对于表格文本的检测和识别以及对于表格结构和单元格坐标的预测，详细说明参考[文档](table/README_ch.md)。
 
-<a name="62"></a>
-
 ### 6.2 DOC-VQA
 
 DOC-VQA指文档视觉问答，其中包括语义实体识别 (Semantic Entity Recognition, SER) 和关系抽取 (Relation Extraction, RE) 任务。基于 SER 任务，可以完成对图像中的文本识别与分类；基于 RE 任务，可以完成对图象中的文本内容的关系提取，如判断问题对(pair)，详细说明参考[文档](vqa/README.md)。
 
-<a name="7"></a>
-
 ## 7. 模型库
 
 PP-Structure系列模型列表（更新中）
 
-* 版面分析模型
+### 7.1 版面分析模型
 
 |模型名称|模型简介|下载地址|
 | --- | --- | --- |
 | ppyolov2_r50vd_dcn_365e_publaynet | PubLayNet 数据集训练的版面分析模型，可以划分**文字、标题、表格、图片以及列表**5类区域 | [PubLayNet](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) |
 
-
-* OCR和表格识别模型
+### 7.2 OCR和表格识别模型
 
 |模型名称|模型简介|模型大小|下载地址|
 | --- | --- | --- | --- |
-|ch_ppocr_mobile_slim_v2.0_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) |
-|ch_ppocr_mobile_slim_v2.0_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
+|ch_PP-OCRv2_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| 3M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar)|
+|ch_PP-OCRv2_rec_slim|【最新】slim量化版超轻量模型，支持中英文、数字识别| 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) |
 |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) |
 
-* DOC-VQA 模型
+### 7.2 DOC-VQA 模型
 
 |模型名称|模型简介|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|[推理模型 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|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar) |
+|ser_LayoutXLM_xfun_zhd|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
+|re_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
 
 
-更多模型下载，可以参考 [PPOCR model_list](../doc/doc_en/models_list.md) and  [PPStructure model_list](./docs/model_list.md)
+更多模型下载，可以参考 [PP-OCR model_list](../doc/doc_en/models_list.md) and  [PP-Structure model_list](./docs/models_list.md)

ppstructure/docs/installation.md

+- [快速安装](#快速安装)
+  - [1. PaddlePaddle 和 PaddleOCR](#1-paddlepaddle-和-paddleocr)
+  - [2. 安装其他依赖](#2-安装其他依赖)
+    - [2.1 版面分析所需  Layout-Parser](#21-版面分析所需--layout-parser)
+    - [2.2  VQA所需依赖](#22--vqa所需依赖)
+
 # 快速安装
 
 ## 1. PaddlePaddle 和 PaddleOCR

ppstructure/docs/kie.md

-
+- [关键信息提取(Key Information Extraction)](#关键信息提取key-information-extraction)
+  - [1. 快速使用](#1-快速使用)
+  - [2. 执行训练](#2-执行训练)
+  - [3. 执行评估](#3-执行评估)
+  - [4. 参考文献](#4-参考文献)
 
 # 关键信息提取(Key Information Extraction)
 
@@ -7,11 +11,6 @@
 SDMGR是一个关键信息提取算法，将每个检测到的文本区域分类为预定义的类别，如订单ID、发票号码，金额等。
 
 
-* [1. 快速使用](#1-----)
-* [2. 执行训练](#2-----)
-* [3. 执行评估](#3-----)
-
-<a name="1-----"></a>
 ## 1. 快速使用
 
 训练和测试的数据采用wildreceipt数据集，通过如下指令下载数据集：
@@ -36,7 +35,6 @@ python3.7 tools/infer_kie.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpo
     <img src="./imgs/0.png" width="800">
 </div>
 
-<a name="2-----"></a>
 ## 2. 执行训练
 
 创建数据集软链到PaddleOCR/train_data目录下：
@@ -50,7 +48,6 @@ ln -s ../../wildreceipt ./
 ```
 python3.7 tools/train.py -c configs/kie/kie_unet_sdmgr.yml -o Global.save_model_dir=./output/kie/
 ```
-<a name="3-----"></a>
 ## 3. 执行评估
 
 ```
@@ -58,7 +55,7 @@ python3.7 tools/eval.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=
 ```
 
 
-**参考文献：**
+## 4. 参考文献
 
 <!-- [ALGORITHM] -->
 

ppstructure/docs/kie_en.md

-
+- [Key Information Extraction(KIE)](#key-information-extractionkie)
+  - [1. Quick Use](#1-quick-use)
+  - [2. Model Training](#2-model-training)
+  - [3. Model Evaluation](#3-model-evaluation)
+  - [4. Reference](#4-reference)
 
 # Key Information Extraction(KIE)
 
@@ -6,13 +10,6 @@ This section provides a tutorial example on how to quickly use, train, and evalu
 
 [SDMGR(Spatial Dual-Modality Graph Reasoning)](https://arxiv.org/abs/2103.14470) is a KIE algorithm that classifies each detected text region into predefined categories, such as order ID, invoice number, amount, and etc.
 
-
-* [1. Quick Use](#1-----)
-* [2. Model Training](#2-----)
-* [3. Model Evaluation](#3-----)
-
-<a name="1-----"></a>
-
 ## 1. Quick Use
 
 [Wildreceipt dataset](https://paperswithcode.com/dataset/wildreceipt) is used for this tutorial. It contains 1765 photos, with 25 classes, and 50000 text boxes, which can be downloaded by wget:
@@ -37,7 +34,6 @@ The visualization results are shown in the figure below:
     <img src="./imgs/0.png" width="800">
 </div>
 
-<a name="2-----"></a>
 ## 2. Model Training
 
 Create a softlink to the folder, `PaddleOCR/train_data`:
@@ -51,7 +47,6 @@ The configuration file used for training is `configs/kie/kie_unet_sdmgr.yml`. Th
 ```shell
 python3.7 tools/train.py -c configs/kie/kie_unet_sdmgr.yml -o Global.save_model_dir=./output/kie/
 ```
-<a name="3-----"></a>
 
 ## 3. Model Evaluation
 
@@ -61,7 +56,7 @@ After training, you can execute the model evaluation with the following command:
 python3.7 tools/eval.py -c configs/kie/kie_unet_sdmgr.yml -o Global.checkpoints=./output/kie/best_accuracy
 ```
 
-**Reference:**
+## 4. Reference
 
 <!-- [ALGORITHM] -->
 

ppstructure/docs/model_list.md → ppstructure/docs/models_list.md

-# Model List
+- [PP-Structure 系列模型列表](#pp-structure-系列模型列表)
+  - [1. LayoutParser 模型](#1-layoutparser-模型)
+  - [2. OCR和表格识别模型](#2-ocr和表格识别模型)
+    - [2.1 OCR](#21-ocr)
+    - [2.2 表格识别模型](#22-表格识别模型)
+  - [3. VQA模型](#3-vqa模型)
+  - [4. KIE模型](#4-kie模型)
+
+# PP-Structure 系列模型列表
+
 
 ## 1. LayoutParser 模型
 
@@ -10,25 +19,33 @@
 
 ## 2. OCR和表格识别模型
 
+### 2.1 OCR
+
 |模型名称|模型简介|推理模型大小|下载地址|
 | --- | --- | --- | --- |
-|ch_ppocr_mobile_slim_v2.0_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) |
-|ch_ppocr_mobile_slim_v2.0_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
 |en_ppocr_mobile_v2.0_table_det|PubLayNet数据集训练的英文表格场景的文字检测|4.7M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_det_train.tar) |
 |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) |
 
-如需要使用其他OCR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`两个字段即可。
+如需要使用其他OCR模型，可以在 [PP-OCR 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`两个字段即可。
+
+### 2.2 表格识别模型
+
+|模型名称|模型简介|推理模型大小|下载地址|
+| --- | --- | --- | --- |
+|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) |
 
 ## 3. VQA模型
 
 |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|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.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|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
+|ser_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
+|re_LayoutXLM_xfun_zh|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
+|ser_LayoutLMv2_xfun_zh|基于LayoutLMv2在xfun中文数据集上训练的SER模型|778M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) |
+|re_LayoutLMv2_xfun_zh|基于LayoutLMv2在xfun中文数据集上训练的RE模型|765M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) |
+|ser_LayoutLM_xfun_zh|基于LayoutLM在xfun中文数据集上训练的SER模型|430M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
 
-## 3. KIE模型
+## 4. KIE模型
 
 |模型名称|模型简介|模型大小|下载地址|
 | --- | --- | --- | --- |
-|SDMGR|关键信息提取模型|-|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|
+|SDMGR|关键信息提取模型|78M|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|

ppstructure/docs/quickstart.md

 # PP-Structure 快速开始
 
-* [1. 安装PaddleOCR whl包](#1)
-* [2. 便捷使用](#2)
-    + [2.1 命令行使用](#21)
-    + [2.2 Python脚本使用](#22)
-    + [2.3 返回结果说明](#23)
-    + [2.4 参数说明](#24)
-* [3. Python脚本使用](#3)
-
-
-<a name="1"></a>
+- [PP-Structure 快速开始](#pp-structure-快速开始)
+  - [1. 安装依赖包](#1-安装依赖包)
+  - [2. 便捷使用](#2-便捷使用)
+    - [2.1 命令行使用](#21-命令行使用)
+    - [2.2 Python脚本使用](#22-python脚本使用)
+    - [2.3 返回结果说明](#23-返回结果说明)
+    - [2.4 参数说明](#24-参数说明)
+  - [3. Python脚本使用](#3-python脚本使用)
 
 ## 1. 安装依赖包
 
@@ -24,12 +22,8 @@ pip3 install -e .
 
 ```
 
-<a name="2"></a>
-
 ## 2. 便捷使用
 
-<a name="21"></a>
-
 ### 2.1 命令行使用
 
 * 版面分析+表格识别
@@ -41,8 +35,6 @@ paddleocr --image_dir=../doc/table/1.png --type=structure
 
 请参考：[文档视觉问答](../vqa/README.md)。
 
-<a name="22"></a>
-
 ### 2.2 Python脚本使用
 
 * 版面分析+表格识别
@@ -76,8 +68,6 @@ im_show.save('result.jpg')
 
 请参考：[文档视觉问答](../vqa/README.md)。
 
-<a name="23"></a>
-
 ### 2.3 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
 
@@ -103,8 +93,6 @@ dict 里各个字段说明如下
 
 请参考：[文档视觉问答](../vqa/README.md)。
 
-<a name="24"></a>
-
 ### 2.4 参数说明
 
 | 字段            | 说明                                     | 默认值                                      |
@@ -122,8 +110,6 @@ 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。
 
-<a name="3"></a>
-
 ## 3. Python脚本使用
 
 * 版面分析+表格识别

ppstructure/layout/README.md

 English | [简体中文](README_ch.md)
-
+- [Getting Started](#getting-started)
+  - [1.  Install whl package](#1--install-whl-package)
+  - [2. Quick Start](#2-quick-start)
+  - [3. PostProcess](#3-postprocess)
+  - [4. Results](#4-results)
+  - [5. Training](#5-training)
 
 # Getting Started
 
-[1. Install whl package](#Install)
-
-[2. Quick Start](#QuickStart)
-
-[3. PostProcess](#PostProcess)
-
-[4. Results](#Results)
-
-[5. Training](#Training)
-
-<a name="Install"></a>
-
 ## 1.  Install whl package
 ```bash
 wget https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
 pip install -U layoutparser-0.0.0-py3-none-any.whl
 ```
 
-<a name="QuickStart"></a>
-
 ## 2. Quick Start
 
 Use LayoutParser to identify the layout of a document:
@@ -77,8 +68,6 @@ The following model configurations and label maps are currently supported, which
 * TableBank word and TableBank latex are trained on datasets of word documents and latex documents respectively;
 * Download TableBank dataset contains both word and latex。
 
-<a name="PostProcess"></a>
-
 ## 3. PostProcess
 
 Layout parser contains multiple categories, if you only want to get the detection box for a specific category (such as the "Text" category), you can use the following code:
@@ -119,7 +108,6 @@ Displays results with only the "Text" category：
 <div align="center">
 <img src="../../doc/table/result_text.jpg"  width = "600" />
 </div>
-<a name="Results"></a>
 
 ## 4. Results
 
@@ -134,8 +122,6 @@ Displays results with only the "Text" category：
 
 ​    **GPU：**  a single NVIDIA Tesla P40
 
-<a name="Training"></a>
-
 ## 5. Training
 
 The above model is based on [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection). If you want to train your own layout parser model，please refer to：[train_layoutparser_model](train_layoutparser_model.md)

ppstructure/layout/README_ch.md

 [English](README.md) | 简体中文
+- [版面分析使用说明](#版面分析使用说明)
+  - [1.  安装whl包](#1--安装whl包)
+  - [2. 使用](#2-使用)
+  - [3. 后处理](#3-后处理)
+  - [4. 指标](#4-指标)
+  - [5. 训练版面分析模型](#5-训练版面分析模型)
 
 # 版面分析使用说明
 
-[1. 安装whl包](#安装whl包)
-
-[2. 使用](#使用)
-
-[3. 后处理](#后处理)
-
-[4. 指标](#指标)
-
-[5. 训练版面分析模型](#训练版面分析模型)
-
-<a name="安装whl包"></a>
-
 ## 1.  安装whl包
 ```bash
 pip install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
 ```
 
-<a name="使用"></a>
-
 ## 2. 使用
 
 使用layoutparser识别给定文档的布局：
@@ -76,8 +68,6 @@ show_img.show()
 * TableBank word和TableBank latex分别在word文档、latex文档数据集训练；
 * 下载的TableBank数据集里同时包含word和latex。
 
-<a name="后处理"></a>
-
 ## 3. 后处理
 
 版面分析检测包含多个类别，如果只想获取指定类别(如"Text"类别)的检测框、可以使用下述代码：
@@ -119,8 +109,6 @@ show_img.show()
 <img src="../../doc/table/result_text.jpg"  width = "600" />
 </div>
 
-<a name="指标"></a>
-
 ## 4. 指标
 
 | Dataset   | mAP  | CPU time cost | GPU time cost |
@@ -134,8 +122,6 @@ show_img.show()
 
 ​    **GPU：**  a single NVIDIA Tesla P40
 
-<a name="训练版面分析模型"></a>
-
 ## 5. 训练版面分析模型
 
 上述模型基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) 训练，如果您想训练自己的版面分析模型，请参考：[train_layoutparser_model](train_layoutparser_model_ch.md)

ppstructure/layout/train_layoutparser_model.md

-# Training layout-parse
-
-[1. Installation](#Installation)
-
-​  [1.1 Requirements](#Requirements)
-
-​  [1.2 Install PaddleDetection](#Install_PaddleDetection)
-
-[2.  Data preparation](#Data_reparation)
-
-[3. Configuration](#Configuration)
+English | [简体中文](train_layoutparser_model_ch.md)
+- [Training layout-parse](#training-layout-parse)
+  - [1.  Installation](#1--installation)
+    - [1.1 Requirements](#11-requirements)
+    - [1.2 Install PaddleDetection](#12-install-paddledetection)
+  - [2. Data preparation](#2-data-preparation)
+  - [3. Configuration](#3-configuration)
+  - [4. Training](#4-training)
+  - [5. Prediction](#5-prediction)
+  - [6. Deployment](#6-deployment)
+    - [6.1 Export model](#61-export-model)
+    - [6.2 Inference](#62-inference)
 
-[4. Training](#Training)
-
-[5. Prediction](#Prediction)
-
-[6. Deployment](#Deployment)
-
-​  [6.1 Export model](#Export_model)
-
-​  [6.2 Inference](#Inference)  
-
-<a name="Installation"></a>
+# Training layout-parse
 
 ## 1.  Installation
 
-<a name="Requirements"></a>
-
 ### 1.1 Requirements
 
 - PaddlePaddle 2.1
@@ -35,8 +24,6 @@
 - CUDA >= 10.1
 - cuDNN >= 7.6
 
-<a name="Install_PaddleDetection"></a>
-
 ### 1.2 Install PaddleDetection
 
 ```bash
@@ -51,8 +38,6 @@ pip install -r requirements.txt
 
 For more installation tutorials, please refer to： [Install doc](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/INSTALL_cn.md)
 
-<a name="Data_preparation"></a>
-
 ## 2. Data preparation
 
 Download the [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) dataset
@@ -80,8 +65,6 @@ PubLayNet directory structure after decompressing ：
 
 For other datasets，please refer to [the PrepareDataSet]((https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/PrepareDataSet.md) )
 
-<a name="Configuration"></a>
-
 ## 3. Configuration
 
 We use the  `configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml` configuration for training，the configuration file is as follows
@@ -113,8 +96,6 @@ The `ppyolov2_r50vd_dcn_365e_coco.yml` configuration depends on other configurat
 
 Modify the preceding files, such as the dataset path and batch size etc.
 
-<a name="Training"></a>
-
 ## 4. Training
 
 PaddleDetection provides single-card/multi-card training mode to meet various training needs of users:
@@ -146,8 +127,6 @@ python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppy
 
 Note: If you encounter "`Out of memory error`" , try reducing `batch_size` in the `ppyolov2_reader.yml`  file
 
-prediction<a name="Prediction"></a>
-
 ## 5. Prediction
 
 Set parameters and use PaddleDetection to predict：
@@ -159,14 +138,10 @@ python tools/infer.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --infer
 
 `--draw_threshold` is an optional parameter. According to the calculation of [NMS](https://ieeexplore.ieee.org/document/1699659), different threshold will produce different results, ` keep_top_k ` represent  the maximum amount of output target, the default value is 10. You can set different value according to your own actual situation。
 
-<a name="Deployment"></a>
-
 ## 6. Deployment
 
 Use your trained model in Layout Parser
 
-<a name="Export_model"></a>
-
 ### 6.1 Export model
 
 n the process of model training, the model file saved contains the process of forward prediction and back propagation. In the actual industrial deployment, there is no need for back propagation. Therefore, the model should be translated into the model format required by the deployment. The `tools/export_model.py` script is provided in PaddleDetection to export the model.
@@ -183,8 +158,6 @@ The prediction model is exported to `inference/ppyolov2_r50vd_dcn_365e_coco` ,in
 
 More model export tutorials, please refer to：[EXPORT_MODEL](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/deploy/EXPORT_MODEL.md)
 
-<a name="Inference"></a>
-
 ### 6.2 Inference
 
 `model_path` represent  the trained model path, and layoutparser is used to predict:
@@ -194,8 +167,6 @@ import layoutparser as lp
 model = lp.PaddleDetectionLayoutModel(model_path="inference/ppyolov2_r50vd_dcn_365e_coco", threshold=0.5,label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},enforce_cpu=True,enable_mkldnn=True)
 ```
 
-
-
 ***
 
 More PaddleDetection training tutorials，please reference：[PaddleDetection Training](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/GETTING_STARTED_cn.md)

ppstructure/layout/train_layoutparser_model_ch.md

-# 训练版面分析
-
-[1. 安装](#安装)
-
-​        [1.1 环境要求](#环境要求)
-
-​        [1.2 安装PaddleDetection](#安装PaddleDetection)
-
-[2. 准备数据](#准备数据)
-
-[3. 配置文件改动和说明](#配置文件改动和说明)
-
-[4. PaddleDetection训练](#训练)
-
-[5. PaddleDetection预测](#预测)
-
-[6. 预测部署](#预测部署)
-
-​        [6.1 模型导出](#模型导出)
-
-​        [6.2 layout parser预测](#layout_parser预测)
+[English](train_layoutparser_model.md) | 简体中文
+- [训练版面分析](#训练版面分析)
+  - [1. 安装](#1-安装)
+    - [1.1 环境要求](#11-环境要求)
+    - [1.2 安装PaddleDetection](#12-安装paddledetection)
+  - [2. 准备数据](#2-准备数据)
+  - [3. 配置文件改动和说明](#3-配置文件改动和说明)
+  - [4. PaddleDetection训练](#4-paddledetection训练)
+  - [5. PaddleDetection预测](#5-paddledetection预测)
+  - [6. 预测部署](#6-预测部署)
+    - [6.1 模型导出](#61-模型导出)
+    - [6.2 layout_parser预测](#62-layout_parser预测)
 
-<a name="安装"></a>
+# 训练版面分析
 
 ## 1. 安装
 
-<a name="环境要求"></a>
-
 ### 1.1 环境要求
 
 - PaddlePaddle 2.1
@@ -35,8 +24,6 @@
 - CUDA >= 10.1
 - cuDNN >= 7.6
 
-<a name="安装PaddleDetection"></a>
-
 ### 1.2 安装PaddleDetection
 
 ```bash
@@ -51,8 +38,6 @@ pip install -r requirements.txt
 
 更多安装教程，请参考: [Install doc](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/INSTALL_cn.md)
 
-<a name="数据准备"></a>
-
 ## 2. 准备数据
 
 下载 [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) 数据集：
@@ -80,8 +65,6 @@ tar -xvf publaynet.tar.gz
 
 如果使用其它数据集，请参考[准备训练数据](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/PrepareDataSet.md)
 
-<a name="配置文件改动和说明"></a>
-
 ## 3. 配置文件改动和说明
 
 我们使用 `configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml`配置进行训练，配置文件摘要如下：
@@ -113,8 +96,6 @@ weights: output/ppyolov2_r50vd_dcn_365e_coco/model_final
 
 根据实际情况，修改上述文件，比如数据集路径、batch size等。
 
-<a name="训练"></a>
-
 ## 4. PaddleDetection训练
 
 PaddleDetection提供了单卡/多卡训练模式，满足用户多种训练需求
@@ -146,8 +127,6 @@ python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppy
 
 注意：如果遇到 "`Out of memory error`" 问题, 尝试在 `ppyolov2_reader.yml` 文件中调小`batch_size`
 
-<a name="预测"></a>
-
 ## 5. PaddleDetection预测
 
 设置参数，使用PaddleDetection预测：
@@ -159,14 +138,10 @@ python tools/infer.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --infer
 
 `--draw_threshold` 是个可选参数. 根据 [NMS](https://ieeexplore.ieee.org/document/1699659) 的计算，不同阈值会产生不同的结果 `keep_top_k`表示设置输出目标的最大数量，默认值为100，用户可以根据自己的实际情况进行设定。
 
-<a name="预测部署"></a>
-
 ## 6. 预测部署
 
 在layout parser中使用自己训练好的模型。
 
-<a name="模型导出"></a>
-
 ### 6.1 模型导出
 
 在模型训练过程中保存的模型文件是包含前向预测和反向传播的过程，在实际的工业部署则不需要反向传播，因此需要将模型进行导成部署需要的模型格式。 在PaddleDetection中提供了 `tools/export_model.py`脚本来导出模型。
@@ -183,8 +158,6 @@ python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml
 
 更多模型导出教程，请参考：[EXPORT_MODEL](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/deploy/EXPORT_MODEL.md)
 
-<a name="layout parser预测"></a>
-
 ### 6.2 layout_parser预测
 
 `model_path`指定训练好的模型路径，使用layout parser进行预测：

ppstructure/table/README.md

+- [Table Recognition](#table-recognition)
+  - [1. pipeline](#1-pipeline)
+  - [2. Performance](#2-performance)
+  - [3. How to use](#3-how-to-use)
+    - [3.1 quick start](#31-quick-start)
+    - [3.2 Train](#32-train)
+    - [3.3 Eval](#33-eval)
+    - [3.4 Inference](#34-inference)
+
+
 # Table Recognition
 
 ## 1. pipeline
@@ -51,10 +61,10 @@ After running, the excel sheet of each picture will be saved in the directory sp
 
 In this chapter, we only introduce the training of the table structure model, For model training of [text detection](../../doc/doc_en/detection_en.md) and [text recognition](../../doc/doc_en/recognition_en.md), please refer to the corresponding documents
 
-#### data preparation  
+* data preparation  
 The training data uses public data set [PubTabNet](https://arxiv.org/abs/1911.10683 ), Can be downloaded from the official [website](https://github.com/ibm-aur-nlp/PubTabNet) 。The PubTabNet data set contains about 500,000 images, as well as annotations in html format。
 
-#### Start training  
+* Start training  
 *If you are installing the cpu version of paddle, please modify the `use_gpu` field in the configuration file to false*
 ```shell
 # single GPU training
@@ -67,7 +77,7 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/
 In the above instruction, use `-c` to select the training to use the `configs/table/table_mv3.yml` configuration file.
 For a detailed explanation of the configuration file, please refer to [config](../../doc/doc_en/config_en.md).
 
-#### load trained model and continue training
+* load trained model and continue training
 
 If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded.
 

ppstructure/table/README_ch.md

-# 表格识别
+- [表格识别](#表格识别)
+  - [1. 表格识别 pipeline](#1-表格识别-pipeline)
+  - [2. 性能](#2-性能)
+  - [3. 使用](#3-使用)
+    - [3.1 快速开始](#31-快速开始)
+    - [3.2 训练](#32-训练)
+    - [3.3 评估](#33-评估)
+    - [3.4 预测](#34-预测)
 
-* [1. 表格识别 pipeline](#1)
-* [2. 性能](#2)
-* [3. 使用](#3)
-  + [3.1 快速开始](#31)
-  + [3.2 训练](#32)
-  + [3.3 评估](#33)
-  + [3.4 预测](#34)
+# 表格识别
 
-<a name="1"></a>
 ## 1. 表格识别 pipeline
 
 表格识别主要包含三个模型
@@ -28,7 +28,6 @@
 4. 单元格的识别结果和表格结构一起构造表格的html字符串。
 
 
-<a name="2"></a>
 ## 2. 性能
 我们在 PubTabNet<sup>[1]</sup> 评估数据集上对算法进行了评估，性能如下
 
@@ -38,9 +37,8 @@
 | EDD<sup>[2]</sup> | 88.3 |
 | Ours | 93.32 |
 
-<a name="3"></a>
 ## 3. 使用
-<a name="31"></a>
+
 ### 3.1 快速开始
 
 ```python
@@ -61,14 +59,17 @@ python3 table/predict_table.py --det_model_dir=inference/en_ppocr_mobile_v2.0_ta
 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
 
 note: 上述模型是在 PubLayNet 数据集上训练的表格识别模型，仅支持英文扫描场景，如需识别其他场景需要自己训练模型后替换 `det_model_dir`,`rec_model_dir`,`table_model_dir`三个字段即可。
-<a name="32"></a>
+
 ### 3.2 训练
+
 在这一章节中，我们仅介绍表格结构模型的训练，[文字检测](../../doc/doc_ch/detection.md)和[文字识别](../../doc/doc_ch/recognition.md)的模型训练请参考对应的文档。
 
-#### 数据准备  
+* 数据准备  
+
 训练数据使用公开数据集PubTabNet ([论文](https://arxiv.org/abs/1911.10683)，[下载地址](https://github.com/ibm-aur-nlp/PubTabNet))。PubTabNet数据集包含约50万张表格数据的图像，以及图像对应的html格式的注释。
 
-#### 启动训练  
+* 启动训练
+
 *如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
 ```shell
 # 单机单卡训练
@@ -79,7 +80,7 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/
 
 上述指令中，通过-c 选择训练使用configs/table/table_mv3.yml配置文件。有关配置文件的详细解释，请参考[链接](../../doc/doc_ch/config.md)。
 
-#### 断点训练
+* 断点训练
 
 如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径：
 ```shell
@@ -88,7 +89,6 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo
 
 **注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrain_weights`指定的模型。
 
-<a name="33"></a>
 ### 3.3 评估
 
 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:
@@ -113,7 +113,6 @@ python3 table/eval_table.py --det_model_dir=path/to/det_model_dir --rec_model_di
 ```bash
 teds: 93.32
 ```
-<a name="34"></a>
 ### 3.4 预测
 
 ```python

ppstructure/vqa/README.md

+- [文档视觉问答（DOC-VQA）](#文档视觉问答doc-vqa)
+  - [1. 简介](#1-简介)
+  - [2. 性能](#2-性能)
+  - [3. 效果演示](#3-效果演示)
+    - [3.1 SER](#31-ser)
+    - [3.2 RE](#32-re)
+  - [4. 安装](#4-安装)
+    - [4.1 安装依赖](#41-安装依赖)
+    - [4.2 安装PaddleOCR（包含 PP-OCR 和 VQA）](#42-安装paddleocr包含-pp-ocr-和-vqa)
+  - [5. 使用](#5-使用)
+    - [5.1 数据和预训练模型准备](#51-数据和预训练模型准备)
+    - [5.2 SER](#52-ser)
+    - [5.3 RE](#53-re)
+  - [6. 参考链接](#6-参考链接)
+
+
 # 文档视觉问答（DOC-VQA）
 
+## 1. 简介
+
 VQA指视觉问答，主要针对图像内容进行提问和回答,DOC-VQA是VQA任务中的一种，DOC-VQA主要针对文本图像的文字内容提出问题。
 
 PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进行开发。
@@ -16,23 +34,23 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进
 本项目是 [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/pdf/2104.08836.pdf) 在 Paddle 2.2上的开源实现，
 包含了在 [XFUND数据集](https://github.com/doc-analysis/XFUND) 上的微调代码。
 
-## 1 性能
+## 2. 性能
 
 我们在 [XFUN](https://github.com/doc-analysis/XFUND) 的中文数据集上对算法进行了评估，性能如下
 
 | 模型 | 任务 | hmean | 模型下载地址 |
 |:---:|:---:|:---:| :---:|
-| LayoutXLM | RE | 0.7483 | [链接](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
 | LayoutXLM | SER | 0.9038 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) |
+| LayoutXLM | RE | 0.7483 | [链接](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) |
+| LayoutLMv2 | SER | 0.8544 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar)
+| LayoutLMv2 | RE | 0.6777 | [链接](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) |
 | LayoutLM | SER | 0.7731 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) |
 
-
-
-## 2. 效果演示
+## 3. 效果演示
 
 **注意：** 测试图片来源于XFUN数据集。
 
-### 2.1 SER
+### 3.1 SER
 
 ![](../../doc/vqa/result_ser/zh_val_0_ser.jpg) | ![](../../doc/vqa/result_ser/zh_val_42_ser.jpg)
 ---|---
@@ -45,8 +63,7 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进
 
 在OCR检测框的左上方也标出了对应的类别和OCR识别结果。
 
-
-### 2.2 RE
+### 3.2 RE
 
 ![](../../doc/vqa/result_re/zh_val_21_re.jpg) | ![](../../doc/vqa/result_re/zh_val_40_re.jpg)
 ---|---
@@ -54,10 +71,9 @@ PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进
 
 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。
 
+## 4. 安装
 
-## 3. 安装
-
-### 3.1 安装依赖
+### 4.1 安装依赖
 
 - **（1) 安装PaddlePaddle**
 
@@ -73,8 +89,7 @@ python3 -m pip install "paddlepaddle>=2.2" -i https://mirror.baidu.com/pypi/simp
 ```
 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。
 
-
-### 3.2 安装PaddleOCR（包含 PP-OCR 和 VQA ）
+### 4.2 安装PaddleOCR（包含 PP-OCR 和 VQA）
 
 - **（1）pip快速安装PaddleOCR whl包（仅预测）**
 
@@ -99,10 +114,9 @@ git clone https://gitee.com/paddlepaddle/PaddleOCR
 python3 -m pip install -r ppstructure/vqa/requirements.txt
 ```
 
-## 4. 使用
+## 5. 使用
 
-
-### 4.1 数据和预训练模型准备
+### 5.1 数据和预训练模型准备
 
 如果希望直接体验预测过程，可以下载我们提供的预训练模型，跳过训练过程，直接预测即可。
 
@@ -125,7 +139,7 @@ wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar
 python3 ppstructure/vqa/helper/trans_xfun_data.py --ori_gt_path=path/to/json_path --output_path=path/to/save_path
 ```
 
-### 4.2 SER任务
+### 5.2 SER
 
 启动训练之前，需要修改下面的四个字段
 
@@ -164,7 +178,7 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/ser/layoutxlm.yml -o
 使用如下命令即可完成`OCR引擎 + SER`的串联预测
 
 ```shell
-CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/layoutxlm.yml  -o Architecture.Backbone.checkpoints=PP-Layout_v1.0_ser_pretrained/ Global.infer_img=doc/vqa/input/zh_val_42.jpg
+CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/layoutxlm.yml  -o Architecture.Backbone.checkpoints=ser_LayoutXLM_xfun_zh/ Global.infer_img=doc/vqa/input/zh_val_42.jpg
 ```
 
 最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。
@@ -178,8 +192,7 @@ export CUDA_VISIBLE_DEVICES=0
 python3 helper/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json  --pred_json_path output_res/infer_results.txt
 ```
 
-
-### 3.3 RE任务
+### 5.3 RE
 
 * 启动训练
 
@@ -219,13 +232,12 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/re/layoutxlm.yml -o
 使用如下命令即可完成`OCR引擎 + SER + RE`的串联预测
 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=PP-Layout_v1.0_re_pretrained/ Global.infer_img=doc/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=PP-Layout_v1.0_ser_pretrained/
+python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=re_LayoutXLM_xfun_zh/ Global.infer_img=doc/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=ser_LayoutXLM_xfun_zh/
 ```
 
 最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。
 
-
-## 参考链接
+## 6. 参考链接
 
 - LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding, https://arxiv.org/pdf/2104.08836.pdf
 - microsoft/unilm/layoutxlm, https://github.com/microsoft/unilm/tree/master/layoutxlm