PaddleOCR/docs/algorithm/super_resolution/algorithm_sr_gestalt.en.md

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Text Gestalt

1. Introduction

Paper:

Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution Chen, Jingye and Yu, Haiyang and Ma, Jianqi and Li, Bin and Xue, Xiangyang AAAI, 2022

Referring to the FudanOCR data download instructions, the effect of the super-score algorithm on the TextZoom test set is as follows:

Model	Backbone	config	Acc	Download link
Text Gestalt	tsrn	19.28	0.6560	configs/sr/sr_tsrn_transformer_strock.yml

2. Environment

Please refer to "Environment Preparation" to configure the PaddleOCR environment, and refer to "Project Clone"to clone the project code.

3. Model Training / Evaluation / Prediction

Please refer to Text Recognition Tutorial. PaddleOCR modularizes the code, and training different models only requires changing the configuration file.

Training

Specifically, after the data preparation is completed, the training can be started. The training command is as follows:

# Single GPU training (long training period, not recommended)
python3 tools/train.py -c configs/sr/sr_tsrn_transformer_strock.yml

# Multi GPU training, specify the gpu number through the --gpus parameter
python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/sr/sr_tsrn_transformer_strock.yml

Evaluation

# GPU evaluation
python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy

Prediction

# The configuration file used for prediction must match the training

python3 tools/infer_sr.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words_en/word_52.png

After executing the command, the super-resolution result of the above image is as follows:

4. Inference and Deployment

4.1 Python Inference

First, the model saved during the training process is converted into an inference model. ( Model download link ), you can use the following command to convert:

python3 tools/export_model.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/sr_out

For Text-Gestalt super-resolution model inference, the following commands can be executed:

python3 tools/infer/predict_sr.py --sr_model_dir=./inference/sr_out --image_dir=doc/imgs_words_en/word_52.png --sr_image_shape=3,32,128

After executing the command, the super-resolution result of the above image is as follows:

4.2 C++ Inference

Not supported

4.3 Serving

Not supported

4.4 More

Not supported

5. FAQ

Citation

@inproceedings{chen2022text,
  title={Text gestalt: Stroke-aware scene text image super-resolution},
  author={Chen, Jingye and Yu, Haiyang and Ma, Jianqi and Li, Bin and Xue, Xiangyang},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  number={1},
  pages={285--293},
  year={2022}
}