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ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer

This is the pytorch implementation of Paper: ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer (ICCV 2023). The paper is available at this link.

News

2024.04.09 We release a new text spotting pipeline Bridge Text Spotting that combines the advantages of end-to-end and two-step text spotting. Code

2023.07.21 Code is available.

Getting Started

Installation

Python 3.8 + PyTorch 1.10.0 + CUDA 11.3 + torchvision=0.11.0 + Detectron2 (v0.2.1) + OpenCV for visualization

conda create -n ESTS python=3.8 -y
conda activate ESTS
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/mxin262/ESTextSpotter.git
cd detectron2-0.2.1
python setup.py build develop
pip install opencv-python
cd models/ests/ops
sh make.sh

Data Preparation

Please download TotalText, CTW1500, MLT, ICDAR2013, ICDAR2015, and CurvedSynText150k according to the guide provided by SPTS v2: README.md.

Please download the MLT 2019 in Images / Annotations.

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

- datasets
  | - CTW1500
  |   | - annotations
  |   | - ctwtest_text_image
  |   | - ctwtrain_text_image
  | - totaltext (or icdar2015)
  |   | - test_images
  |   | - train_images
  |   | - test.json
  |   | - train.json
  | - mlt2017 (or syntext1, syntext2)
      | - annotations
      | - images

Model Zoo

Dataset	Det-P	Det-R	Det-F1	E2E-None	E2E-Full	Weights
Pretrain	90.7	85.3	87.9	73.8	85.5	OneDrive
Total-Text	91.8	88.2	90.0	80.9	87.1	OneDrive
CTW1500	91.3	88.6	89.9	65.0	83.9	OneDrive

Dataset	Det-P	Det-R	Det-F1	E2E-S	E2E-W	E2E-G	Weights
ICDAR2015	95.1	88	91.4	88.5	83.1	78.1	OneDrive

Dataset	H-mean	Weights
VinText	73.6	OneDrive

Dataset	Det-P	Det-R	Det-H	1-NED	Weights
ICDAR 2019 ReCTS	94.1	91.3	92.7	78.1	OneDrive

Dataset	R	P	H	AP	Arabic	Latin	Chinese	Japanese	Korean	Bangla	Hindi	Weights
MLT	75.5	83.37	79.24	72.52	52.00	77.34	48.20	48.42	63.56	38.26	50.83	OneDrive

Training

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

Pretrain

bash scripts/Pretrain.sh /path/to/your/dataset

Fine-tune model on the mixed real dataset

bash scripts/Joint_train.sh /path/to/your/dataset

Fine-tune model

bash scripts/TT_finetune.sh /path/to/your/dataset

Evaluation

0 for Text Detection; 1 for Text Spotting.

bash scripts/test.sh config/ESTS/ESTS_5scale_tt_finetune.py /path/to/your/dataset 1 /path/to/your/checkpoint /path/to/your/test dataset

e.g.:

bash scripts/test.sh config/ESTS/ESTS_5scale_tt_finetune.py ../datasets 1 totaltext_checkpoint.pth totaltext_val

Visualization

Visualize the detection and recognition results

python vis.py

Example Results:

Copyright

This repository can only be used for non-commercial research purpose.

For commercial use, please contact Prof. Lianwen Jin (eelwjin@scut.edu.cn).

Acknowlegement

AdelaiDet, DINO, Detectron2, TESTR

Citation

If our paper helps your research, please cite it in your publications:

@InProceedings{Huang_2023_ICCV,
    author    = {Huang, Mingxin and Zhang, Jiaxin and Peng, Dezhi and Lu, Hao and Huang, Can and Liu, Yuliang and Bai, Xiang and Jin, Lianwen},
    title     = {ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {19495-19505}
}