Paragraph2Graph: A Language-independent GNN-based framework for layout analysis

This is the official code released repo for Paragraph2Graph: A Language-independent GNN-based framework for layout analysis

Introduction

We propose a language-independent GNN framework for document layout analysis tasks. Our proposed model, Paragraph2Graph, uses a pre-trained CNN to encode image features and incorporates 2d OCR text coordinates and image features as node features in a graph. We use a dynamic graph convolutional neural network (DGCNN) to update the graph based on these features and include edge features based on relationships. With only 19.95 million parameters, our model is suitable for industrial applications, particularly in multi-language scenarios.

How to Use

1. Set up the environment

If you are using pytorch==2.0.1, you can simply set up the environment by

pip install -r requirements-dev.txt

If you are using an older pytorch==1.12.1, make sure to edit requirements.txt by replacing -r requirements/requirements-dependency-2.0.1.txt with -r requirements/requirements-dependency-1.12.1.txt

2. Prepare dataset

Let's use the DocLayNet dataset as an example:

1. Please download the DocLayNet core dataset & DocLayNet extra files and merge them into one folder.

Doclaynet core dataset should be like

├── COCO
│   ├── test.json
│   ├── train.json
│   └── val.json
├── PNG
│   ├── <hash>.png
│   ├── ...
├── PDF
│   ├── <hash>.pdf
│   ├── ...
├── JSON
│   ├── <hash>.json
│   ├── ...

2. Pre-process the dataset with the scripts/preprocess_data/test_convert_DocLayNet2Graph

   python mytools/preprocess_to_graph.py --dataset doclaynet # you can change to publaynet or funsd

3. After processing, you can find a DocLayNet_core_graph_labels folder under your specified output dir
4. Make sure to edit the config/train_layout_graph_doclaynet.yaml

datasets:
  train:
    dataset:
      data_root:
        - /your/path/to/DocLayNet_core
      label_root:
        - /your/path/to/DocLayNet_core_graph_labels/train
    collate_fn:
      aug_flag: false
  eval:
    dataset:
      data_root:
        - /your/path/to/DocLayNet_core
      label_root:
        - /your/path/to/DocLayNet_core_graph_labels/val

For preprocessing other datasets, you can find scripts under scripts/preprocess_data/test_convert_Funsd2Graph and scripts/preprocess_data/test_convert_Publaynet2Graph for FUNSD and Publaynet, respectively. The evaluation/prediction process assumes that all datasets adhere to this format. Please ensure to preprocess your custom dataset to conform to this format before proceeding with evaluation.

3. Start training

You can start the training from mytools/train_graph.py or

python mytools/train_graph.py --config_file config/xxxx.yaml --phase train # you can use your own config file

The training script also support ddp with huggingface/accelerate by

accelerate mytools/train_graph.py --config_file config/base.yaml --use_accelerate True

4. Inference

After training, you can set phase to predict in mytools/train_graph.py and specify the path to your trained model in the config file, such as :

model:
  model_path: # put the path to your models here

About config files

config file can specify which yaml it wants to override, such as

base: config/graph_net/train_layout_graph_doclaynet.yaml

Take config/graph_net/train_layout_graph.yaml as an example, the hierarchical override line appears as follows:

base.yaml

   ⬇️  overrided by
   
graph_net/base.yaml

   ⬇️  overrided by
   
config/graph_net/train_layout_graph.yaml

   ⬇️  overrided by
   
config/graph_net/train_layout_graph_doclaynet.yaml

Q & A

Q: how do I set hyper-parameters (1.node_weights 2.edge_weight) in config file?

A: The weight of the node needs to be carefully adjusted to obtain a desirable outcome. Empirically, classes with fewer samples should be given a higher weight, and vice versa. If you are working with Doclaynet, FUNSD or PubLaynet, you can use our hyper-parameters as we set in config/graph_net. If you want to train it with your own dataset, a good choice is to use batch-wise dynamic edge weight: For example, in one batch, you have 2 negative edges and 1 position edge, in order to avoid the negative edges dominating the loss, the negative edge weight can be $1/(2+1)$ and positive edge weight is set to $2/(2+1)$

Q: What is the difference between GraphLayoutDataset and GraphLayoutEntityDataset in mydatasets/gragh_net/layout_dataset.py, and between GraphCollateFn and GraphEntityCollateFn in mydatasets/gragh_net/graph_collate.py, which one should I choose for my use case?

A:

• For entity-node:

datasets:
  train:
    dataset:
      type: GraphLayoutDataset
    collate_fn:
      type: GraphCollateFn

• For entity-linking

datasets:
  train:
    dataset:
      type: GraphLayoutEntityDataset
    collate_fn:
      type: GraphEntityCollateFn

please check this issue for more details. Ensure to change the dataset type and collect_fn type in your config file for your use case.

Citation

f you find our work helpful, please consider citing our work and leaving us a star.

@article{wei2023paragraph2graph,
  title={PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis},
  author={Wei, Shu and Xu, Nuo},
  journal={arXiv preprint arXiv:2304.11810},
  year={2023}
}