DocILE: Document Information Localization and Extraction Benchmark With LiLt

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This repository contains our source code of both Task 1 and Task 2 in the DocILE Competition.

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Introduction

DocILE is a large-scale research benchmark for cross-evaluation of machine learning methods for Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR) from semi-structured business documents such as invoices, orders etc. Such large-scale benchmark was previously missing (Skalický et al., 2022), hindering comparative evaluation.

• This repository this repository contain our source code to use LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding

Installing

Requirements

We're using Python 3.9 to run this repo: Requirements packages:

• poppler-utils
• tensorboard
• jsonargparse[signatures]
• tensorrt==8.5.1.7
• timm
• transformers==4.26.0
• datasets==2.11.0

Install

Run this command to install requirements:

pip install -r requirements.txt

Config

• Change config in ./config/train.cfg to suit your enviroments.
• Change path to config file in ./run_training.sh and ./run_inference.sh to suit your enviroments.

Resplit dataset

We decided to randomly resplit dataset, 80% for train and 20% for validation to have better score. To randomly resplit dataset, change dataset_path in ./data_split.py and run:

python3 data_split.py

Train

Run ./run_training.sh

bash run_training.sh

The models will be save at OUPUT_DIR (config)

Inference

To run inference, follow below instruction.

• On test set, call:

bash run_inference.sh test

• On validation set, call:

bash run_inference.sh val

The result will be save at PREDICTION_DIR (con)

Contributing

Links

https://huggingface.co/docs/transformers/model_doc/lilt

https://docile.rossum.ai/