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requirements.txt | 9 months ago | |
run_model_server.py | 10 months ago |
本模型是以自底向上的方式,先检测文本块和文字行之间的吸引排斥关系,然后对文本块聚类成行,最终输出文字行的外接框的坐标值。
SegLink++模型介绍,详见: Seglink++: Detecting dense and arbitrary-shaped scene text by instance-aware component grouping
模型来源: https://www.modelscope.cn/models/damo/cv_resnet18_ocr-detection-line-level_damo/summary
引用:
@article{tang2019seglink++,
title={Seglink++: Detecting dense and arbitrary-shaped scene text by instance-aware component grouping},
author={Tang, Jun and Yang, Zhibo and Wang, Yongpan and Zheng, Qi and Xu, Yongchao and Bai, Xiang},
journal={Pattern recognition},
volume={96},
pages={106954},
year={2019},
publisher={Elsevier}
}
本模型基于 ServiceBoot微服务引擎 进行服务化封装,参见: 《CubeAI模型开发指南》
$ sh pip-install-reqs.sh
$ serviceboot start
或
$ python3 run_model_server.py
一键式本地容器化部署和运行,参见: 《CubeAI模型独立部署指南》 或 CubeAI Docker Builder
本模型服务可一键发布至 CubeAI智立方平台 进行共享和部署,参见: 《CubeAI模型发布指南》
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