Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs

TGDoc: Text-Grounding Document Understanding with MLLMs

🚀 Overview

TGDoc is a model that enhances document understanding by implementing text-grounding capabilities in Multimodal Large Language Models (MLLMs). Our approach improves the ability to process and comprehend document-based information.

🛠️ Quick Start

Prerequisites

This project builds upon LLaVA. Set up your environment:

git clone https://github.com/haotian-liu/LLaVA.git
cd LLaVA

conda create -n llava python=3.10 -y
conda activate llava

pip install --upgrade pip
pip install -e .
pip install flash-attn --no-build-isolation

Dependencies

deepspeed==0.9.5
peft==0.4.0
transformers==4.31.0
accelerate==0.21.0
bitsandbytes==0.41.0

Resources

Pretrained Models

Model Name	Download Link	Access Code
tgdoc-7b-finetune-336	Download	xhif

Dataset

• Complete dataset: Download (Access Code: gxqt)
• Includes LLaVA and LLaVAR datasets

Dataset Examples

• PPT Data Examples:

• CC3M Finetuning Examples:

Usage

Training

1. Configure settings in llava/data/config.py
2. Select appropriate training script: bash scripts/pretrain.sh and bash scripts/finetune.sh

Inference

python test.py

We use MultimodalOCR for validation, appending each question with: "Support your reasoning with the coordinates [xmin, ymin, xmax, ymax]"

Results

Qualitative Examples

Citation

@article{wang2023towards,
  title={Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs},
  author={Wang, Yonghui and Zhou, Wengang and Feng, Hao and Zhou, Keyi and Li, Houqiang},
  journal={arXiv preprint arXiv:2311.13194},
  year={2023}
}

Acknowledgments

This project builds upon LLaVA. We thank the original authors for their great work.