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CT

1. Introduction

Paper:

CentripetalText: An Efficient Text Instance Representation for Scene Text Detection Tao Sheng, Jie Chen, Zhouhui Lian NeurIPS, 2021

On the Total-Text dataset, the text detection result is as follows:

Model Backbone Configuration Precision Recall Hmean Download
CT ResNet18_vd configs/det/det_r18_vd_ct.yml 88.68% 81.70% 85.05% trained model

2. Environment

Please prepare your environment referring to prepare the environment and clone the repo.

3. Model Training / Evaluation / Prediction

The above CT model is trained using the Total-Text text detection public dataset. For the download of the dataset, please refer to Total-Text-Dataset. PaddleOCR format annotation download link train.txt, test.txt.

Please refer to text detection training tutorial. PaddleOCR has modularized the code structure, so that you only need to replace the configuration file to train different detection models.

4. Inference and Deployment

4.1 Python Inference

First, convert the model saved in the CT text detection training process into an inference model. Taking the model based on the Resnet18_vd backbone network and trained on the Total Text English dataset as example (model download link), you can use the following command to convert:

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python3 tools/export_model.py -c configs/det/det_r18_vd_ct.yml -o Global.pretrained_model=./det_r18_ct_train/best_accuracy  Global.save_inference_dir=./inference/det_ct

CT text detection model inference, you can execute the following command:

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python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_ct/" --det_algorithm="CT"

The visualized text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with det_res. Examples of results are as follows:

img

4.2 C++ Inference

Not supported

4.3 Serving

Not supported

4.4 More

Not supported

5. FAQ

Citation

@inproceedings{sheng2021centripetaltext,
    title={CentripetalText: An Efficient Text Instance Representation for Scene Text Detection},
    author={Tao Sheng and Jie Chen and Zhouhui Lian},
    booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
    year={2021}
}

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