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English | 中文

TableMaster

TableMaster: PINGAN-VCGROUP’S SOLUTION FOR ICDAR 2021 COMPETITION ON SCIENTIFIC LITERATURE PARSING TASK B: TABLE RECOGNITION TO HTML

1. Introduction

TableMaster is a model used for table recognition, and its uniqueness lies in its ability trained for simultaneously recognizing both the position of text blocks within cells and the structure of the table. Traditional methods for table recognition usually involve regressing the coordinates of cells and then obtaining the row and column information based on those coordinates. However, it becomes challenging to directly obtain cell positions or table line information in cases where there are no table lines. TableMaster proposes a new solution by simultaneously learning the position of text blocks within cells and the structure of the table during the training process, using a representation based on Hypertext Markup Language (HTML).

The network architecture consists of two parts: encoding and decoding. In the encoding phase, the image is transformed into sequential features using residual connections and multi-head channel attention modules, facilitating the subsequent decoding process. The sequential features outputted from the encoding phase are then inputted into the decoding phase after position encoding. The decoding part of TableMaster is similar to Master but with an additional branch. After a Transformer layer, the decoding part splits into two branches, each going through two more Transformer layers. These two branches handle two learning tasks respectively: regression of cell text boxes and prediction of the table structure sequence.

Through this approach, TableMaster is able to simultaneously learn and predict the position of cells and the structure of the table, thereby improving the accuracy and effectiveness of table recognition. Its unique design and training strategy enable accurate retrieval of table information even in scenarios without table lines, making it applicable in a wide range of contexts.

Figure 1. Overall TableMaster architecture [1]

2. Results

PubTabNet

Model Context Backbone Accuracy Train T. per step time Throughput Recipe Download
TableMaster D910x8-MS2.2-G TableResNetExtra 77.43% 2100 s/epoch 335 ms/step 238 img/s yaml ckpt

Notes：

  • Context: Training context denoted as {device}x{pieces}-{MS version}{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.
  • The training time of EAST is highly affected by data processing and varies on different machines.
  • The input_shape for exported MindIR in the link is (1,3,480,480).

3. Quick Start

3.1 Installation

Please refer to the installation instruction in MindOCR.

3.2 Dataset preparation

3.2.1 PubTabNet dataset

Please download PubTabNet dataset, unzip the zip files, and split the annotation file into training set and validation set according to the split key in the PubTabNet_2.0.0.jsonl file.

The prepared dataset file struture should be:

PubTabNet
├── train
│   ├── PMC1064074_007_00.png
│   ├── PMC1064076_003_00.png
│   ├── PMC1064076_004_00.png
│   └── ....png
│  
├── val
│   ├── PMC1064865_002_00.png
│   ├── PMC1079806_002_00.png
│   ├── PMC1079811_004_00.png
│   └── ....png
│
├── PubTabNet_2.0.0_train.jsonl
│
└── PubTabNet_2.0.0_val.jsonl

3.3 配置说明

Update configs/table/table_master.yamlconfiguration file with data paths, specificallydataset_root is the directory of training set images folder, label_file_list is a list of training set annotation file path, and it may include multiple annotation file paths.

...
train:
  ckpt_save_dir: './tmp_table'
  dataset_sink_mode: False
  dataset:
    type: PubTabDataset
    dataset_root: dir/to/train                             # <--- Update
    label_file_list: [dir/to/PubTabNet_2.0.0_train.jsonl]  # <--- Update
    sample_ratio_list: [ 1.0 ]
...
eval:
  dataset_sink_mode: False
  dataset:
    type: PubTabDataset
    dataset_root: dir/to/val                               # <--- Update
    label_file_list: [dir/to/PubTabNet_2.0.0_val.jsonl]    # <--- Update
    sample_ratio_list: [ 1.0 ]
...

Optionally, change num_workers according to the number of CPU cores.

TableMaster consists of 2 parts: backbone and head. Specifically:

model:
  type: table
  transform: null
  backbone:
    name: table_resnet_extra
    gcb_config:
      ratio: 0.0625
      headers: 1
      att_scale: False
      fusion_type: channel_add
      layers: [ False, True, True, True ]
    layers: [ 1,2,5,3 ]
  head:
    name: TableMasterHead
    out_channels: 43
    hidden_size: 512
    headers: 8
    dropout: 0.
    d_ff: 2024
    max_text_length: *max_text_len
    loc_reg_num: &loc_reg_num 4

3.4 Training

  • Standalone training

Please set distribute to False in yaml config file .

# train tablemaster on pubtabnet dataset
python tools/train.py --config configs/table/table_master.yaml
  • Distributed training

Please set distribute in yaml config file to be True.

# n is the number of GPUs/NPUs
mpirun --allow-run-as-root -n 8 python tools/train.py --config configs/table/table_master.yaml

The training result (including checkpoints, per-epoch performance and curves) will be saved in the directory parsed by the arg ckpt_save_dir in yaml config file. The default directory is ./tmp_table.

3.5 Evaluation

To evaluate the accuracy of the trained model, you can use eval.py. Please set the checkpoint path to the arg ckpt_load_path in the eval section of yaml config file, set distribute to be False, and then run:

python tools/eval.py --config configs/table/table_master.yaml