DocumentAIKit/doc/doc_en/algorithm_sr_gestalt_en.md

Text Gestalt

• 1. Introduction
• 2. Environment
• 3. Model Training / Evaluation / Prediction
  • 3.1 Training
  • 3.2 Evaluation
  • 3.3 Prediction
• 4. Inference and Deployment
  • 4.1 Python Inference
  • 4.2 C++ Inference
  • 4.3 Serving
  • 4.4 More
• 5. FAQ

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|train model|

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}
}