E2E-MLT

E2E-MLT -- 用于多语言场景文本的不受约束的端到端方法。「E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text」

Github星跟踪图

E2E-MLT

E2E-MLT -- 用于多语言场景文本的不受约束的端到端方法,代码库为:https://arxiv.org/abs/1801.09919

@@inproceedings{buvsta2018e2e,
  title={E2E-MLT-an unconstrained end-to-end method for multi-language scene text},
  author={Bu{\v{s}}ta, Michal and Patel, Yash and Matas, Jiri},
  booktitle={Asian Conference on Computer Vision},
  pages={127--143},
  year={2018},
  organization={Springer}
}

要求

预训练模型

e2e-mlt, e2e-mlt-rctw

wget http://ptak.felk.cvut.cz/public_datasets/SyntText/e2e-mlt.h5

运行演示

python3 demo.py -model=e2e-mlt.h5

数据

MLT SynthSet

合成文本是使用“Synthetic Data for Text Localisation in Natural Images(用于自然图像中文本本地化的合成数据)”生成的,对阿拉伯语和孟加拉语脚本渲染进行了较小的更改。

我们发现有用的东西:

训练

python3 train.py -train_list=sample_train_data/MLT/trainMLT.txt -batch_size=8 -num_readers=5 -debug=0 -input_size=512 -ocr_batch_size=256 -ocr_feed_list=sample_train_data/MLT_CROPS/gt.txt

致谢

代码从 EASTDeepTextSpotter 借用。


(The first version translated by vz on 2020.08.05)

主要指标

概览
名称与所有者MichalBusta/E2E-MLT
主编程语言C++
编程语言Python (语言数: 6)
平台Linux, Mac, Windows
许可证MIT License
所有者活动
创建于2018-10-16 13:12:19
推送于2025-01-08 09:27:36
最后一次提交2022-08-20 10:15:29
发布数0
用户参与
星数294
关注者数15
派生数83
提交数39
已启用问题?
问题数76
打开的问题数31
拉请求数3
打开的拉请求数0
关闭的拉请求数0
项目设置
已启用Wiki?
已存档?
是复刻?
已锁定?
是镜像?
是私有?

E2E-MLT

E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text
code base for: https://arxiv.org/abs/1801.09919

@@inproceedings{buvsta2018e2e,
  title={E2E-MLT-an unconstrained end-to-end method for multi-language scene text},
  author={Bu{\v{s}}ta, Michal and Patel, Yash and Matas, Jiri},
  booktitle={Asian Conference on Computer Vision},
  pages={127--143},
  year={2018},
  organization={Springer}
}

Requirements

Pretrained Models

e2e-mlt, e2e-mlt-rctw

wget http://ptak.felk.cvut.cz/public_datasets/SyntText/e2e-mlt.h5

Running Demo

python3 demo.py -model=e2e-mlt.h5

Data

MLT SynthSet

Synthetic text has been generated using Synthetic Data for Text Localisation in Natural Images, with minor changes for Arabic and Bangla script rendering.

What we have found useful:

  • for generating Arabic Scene Text: https://github.com/mpcabd/python-arabic-reshaper
  • for generating Bangla Scene Text: PyQt4
  • having somebody who can read non-latin scripts: we would like to thank Ali Anas for reviewing generated Arabic scene text.

Training

python3 train.py -train_list=sample_train_data/MLT/trainMLT.txt -batch_size=8 -num_readers=5 -debug=0 -input_size=512 -ocr_batch_size=256 -ocr_feed_list=sample_train_data/MLT_CROPS/gt.txt

Acknowledgments

Code borrows from EAST and DeepTextSpotter