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VimTS: A Unified Video and Image Text Spotter

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# VimTS

<h3 align="center"> <a href="https://arxiv.org/abs/2404.19652">VimTS: A Unified Video and Image Text Spotter for Enhancing the Cross-domain Generalization</a></h3>

[![arXiv](https://img.shields.io/badge/Arxiv-2404.19652-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2404.19652)
[![Project page](https://img.shields.io/badge/Project-Page-white)](https://vimtextspotter.github.io/) 
[![GitHub issues](https://img.shields.io/github/issues/Yuliang-Liu/VimTS?color=critical&label=Issues)](https://github.com/Yuliang-Liu/VimTS/issues?q=is%3Aopen+is%3Aissue)
[![GitHub closed issues](https://img.shields.io/github/issues-closed/Yuliang-Liu/VimTS?color=success&label=Issues)](https://github.com/Yuliang-Liu/VimTS/issues?q=is%3Aissue+is%3Aclosed) <br>
</h5>

# Description

VimTS is a unified video and image text spotter for enhancing the cross-domain generalization. It outperforms the state-of-the-art method by an average of 2.6% in six cross-domain benchmarks such as TT-to-IC15, CTW1500-to-TT, and TT-to-CTW1500. For video-level cross-domain adaption, our method even surpasses the previous end-to-end video spotting method in ICDAR2015 video and DSText v2 by an average of 5.5% on the MOTA metric, using only image-level data.

## News 
* ```2024.5.3 ``` 🚀 Code available.
* ```2024.5.1 ``` 🚀 Release paper [VimTS](https://arxiv.org/abs/2404.19652).

## Framework

Overall framework of our method.

Overall framework of CoDeF-based synthetic method.

## VTD-368K

We manually collect and filter text-free, open-source and unrestricted videos from NExT-QA, Charades-Ego, Breakfast, A2D, MPI-Cooking, ActorShift and Hollywood. By utilizing the CoDeF, our synthetic method facilitates the achievement of realistic and stable text flow propagation, significantly reducing the occurrence of distortions.

## Compared with MLMMs

## Getting Started

- ### Installation
Python 3.8 + PyTorch 1.10.0 + CUDA 11.3 + torchvision=0.11.0 + Detectron2 (v0.2.1) + OpenCV for visualization
```shell
conda create -n VimTS python=3.8 -y
conda activate VimTS
conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install -r requirements.txt
git clone https://github.com/Yuliang-Liu/VimTS.git
cd detectron2-0.2.1
python setup.py build develop
pip install opencv-python
cd models/vimts/ops
sh make.sh
```

### Data Preparation

Please download TotalText, CTW1500, and ICDAR2015 according to the guide provided by SPTS v2: [README.md](https://github.com/bytedance/SPTSv2#dataset).

Please download Ground Truth of line-level [TotalText](https://1drv.ms/u/c/030dd3c816e366d8/ERYss1hPOs1MtiNFBbTj5JIB1YSpQpvbctB5Nz859axAxA?e=Gxs2H8) and Ground Truth of word-level [CTW1500](https://1drv.ms/u/c/030dd3c816e366d8/EZq2H7NOCf9BsCnl0HOyeFgBNL0gEZDQ6gfW4deJcT3w5g?e=HDkfrf).

Extract all the datasets and make sure you organize them as follows

### Training 
We use 8 GPUs for training and 2 images each GPU by default.

```
bash scripts/multi_tasks.sh /path/to/your/dataset
```

### Evaluation 
Download the weight [Google Drive](https://drive.google.com/file/d/14ejhHhBJGiA1N55U_0o4__OWFyZ69pBN/view?usp=sharing).

0 for Text Detection; 1 for Text Spotting.
```
bash scripts/test.sh config/VimTS/VimTS_multi_finetune.py /path/to/your/dataset 1 /path/to/your/checkpoint /path/to/your/test_dataset
```
e.g.:
```
bash scripts/test.sh config/VimTS/VimTS_multi_finetune.py ../datasets 1 cross_domain_checkpoint.pth totaltext_val
```
### Visualization 
Visualize the detection and recognition results
```
python vis.py
```

## Cite
If you wish to refer to the baseline results published here, please use the following BibTeX entries:

```BibTeX
@misc{liuvimts,
          author={Liu, Yuliang and Huang, Mingxin and Yan, Hao and Deng, Linger and Wu, Weijia and Lu, Hao and Shen, Chunhua and Jin, Lianwen and Bai, Xiang},
          title={VimTS: A Unified Video and Image Text Spotter for Enhancing the Cross-domain Generalization}, 
          publisher={arXiv preprint arXiv:2404.19652},
          year={2024},
}
```

## Copyright
We welcome suggestions to help us improve the VimTS. For any query, please contact Prof. Yuliang Liu: ylliu@hust.edu.cn. If you find something interesting, please also feel free to share with us through email or open an issue. Thanks!

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