2024-07-24 20:00:15 +08:00
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typora-copy-images-to: images
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comments: true
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---
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# CT
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## 1. 算法简介
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论文信息：
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> [CentripetalText: An Efficient Text Instance Representation for Scene Text Detection](https://arxiv.org/abs/2107.05945)
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> Tao Sheng, Jie Chen, Zhouhui Lian
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> NeurIPS, 2021
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在Total-Text文本检测公开数据集上，算法复现效果如下：
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|模型|骨干网络|配置文件|precision|recall|Hmean|下载链接|
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| --- | --- | --- | --- | --- | --- | --- |
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2024-08-30 18:35:25 +08:00
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|CT|ResNet18_vd|[configs/det/det_r18_vd_ct.yml](https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/det/det_r18_vd_ct.yml)|88.68%|81.70%|85.05%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r18_ct_train.tar)|
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2024-07-24 20:00:15 +08:00
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## 2. 环境配置
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请先参考[《运行环境准备》](../../ppocr/environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](../../ppocr/blog/clone.md)克隆项目代码。
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## 3. 模型训练、评估、预测
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CT模型使用Total-Text文本检测公开数据集训练得到，数据集下载可参考 [Total-Text-Dataset](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset), 我们将标签文件转成了paddleocr格式，转换好的标签文件下载参考[train.txt](https://paddleocr.bj.bcebos.com/dataset/ct_tipc/train.txt), [text.txt](https://paddleocr.bj.bcebos.com/dataset/ct_tipc/test.txt)。
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请参考[文本检测训练教程](../../ppocr/model_train/detection.md)。PaddleOCR对代码进行了模块化，训练不同的检测模型只需要**更换配置文件**即可。
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## 4. 推理部署
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### 4.1 Python推理
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首先将CT文本检测训练过程中保存的模型，转换成inference model。以基于Resnet18_vd骨干网络，在Total-Text英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r18_ct_train.tar) )，可以使用如下命令进行转换：
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```bash linenums="1"
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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
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```
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CT文本检测模型推理，可以执行如下命令：
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```bash linenums="1"
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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"
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```
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可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为`det_res`。结果示例如下：
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### 4.2 C++推理
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暂不支持
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### 4.3 Serving服务化部署
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暂不支持
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### 4.4 更多推理部署
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暂不支持
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## 5. FAQ
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## 引用
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```bibtex
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@inproceedings{sheng2021centripetaltext,
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title={CentripetalText: An Efficient Text Instance Representation for Scene Text Detection},
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author={Tao Sheng and Jie Chen and Zhouhui Lian},
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booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
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year={2021}
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}
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```
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