PaddleOCR/doc/doc_en/algorithm_sr_telescope_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:

Scene Text Telescope: Text-Focused Scene Image Super-Resolution

Chen, Jingye, Bin Li, and Xiangyang Xue

CVPR, 2021

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	21.56	0.7411	configs/sr/sr_telescope.yml

The TextZoom dataset comes from two superfraction data sets, RealSR and SR-RAW, both of which contain LR-HR pairs. TextZoom has 17367 pairs of training data and 4373 pairs of test data.

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_telescope.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_telescope.yml

Evaluation:

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

For Text-Telescope 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{9578891,
  author={Chen, Jingye and Li, Bin and Xue, Xiangyang},
  booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  title={Scene Text Telescope: Text-Focused Scene Image Super-Resolution},
  year={2021},
  volume={},
  number={},
  pages={12021-12030},
  doi={10.1109/CVPR46437.2021.01185}}