- 1. Introduction
- 2. Environment
- 3. Model Training / Evaluation / Prediction
- 4. Inference and Deployment
- 5. FAQ
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 |
Please prepare your environment referring to prepare the environment and clone the repo.
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.
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:
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:
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:
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@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}
}