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CTPN + DenseNet + CTC based end-to-end Chinese OCR implemented using tensorflow and keras
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README.md

简介

基于Tensorflow和Keras实现端到端的不定长中文字符检测和识别

  • 文本检测:CTPN
  • 文本识别:DenseNet + CTC

环境部署

sh setup.sh
  • 注:CPU环境执行前需注释掉for gpu部分,并解开for cpu部分的注释

Demo

将测试图片放入test_images目录,检测结果会保存到test_result中

python demo.py

模型训练

CTPN训练

详见ctpn/README.md

DenseNet + CTC训练

1. 数据准备

数据集:https://pan.baidu.com/s/1QkI7kjah8SPHwOQ40rS1Pw (密码:lu7m)

  • 共约364万张图片,按照99:1划分成训练集和验证集
  • 数据利用中文语料库(新闻 + 文言文),通过字体、大小、灰度、模糊、透视、拉伸等变化随机生成
  • 包含汉字、英文字母、数字和标点共5990个字符
  • 每个样本固定10个字符,字符随机截取自语料库中的句子
  • 图片分辨率统一为280x32

图片解压后放置到train/images目录下,描述文件放到train目录下

2. 训练

cd train
python train.py

3. 结果

val acc predict model
0.983 8ms 18.9MB
  • GPU: GTX TITAN X
  • Keras Backend: Tensorflow

4. 生成自己的样本

可参考SynthText_Chinese_versionTextRecognitionDataGeneratortext_renderer

效果展示

参考

[1] https://github.com/eragonruan/text-detection-ctpn

[2] https://github.com/senlinuc/caffe_ocr

[3] https://github.com/chineseocr/chinese-ocr

[4] https://github.com/xiaomaxiao/keras_ocr

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