BaiXiang——【arXi2015】An End-to-End Trainable Neural Network for Image-based Sequence Recognition and
2016-11-06 14:07
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BaiXiang的CRNN论文阅读
1. 论文题目BaiXiang——【arXiv2015】An End-to-End Trainable Neural Network forImage-based Sequence Recognition and Its Application to Scene Text Recognition
2. 论文思路和方法
1) 问题范围： 单词识别
2) CNN层：使用标准CNN提取图像特征，利用Map-to-Sequence表示成特征向量；
3) RNN层：使用双向LSTM识别特征向量，得到每列特征的概率分布；
4) Transcription层：利用CTC和前向后向算法求解最优的label序列；
3. 亮点和创新点
1) 端到端可训练（把CNN和RNN联合训练）
2) 任意长度的输入（图像宽度任意，单词长度任意）
3) 训练集无需有字符的标定
4) 带字典和不带字典的库（样本）都可以使用
5) 性能好，而且模型小（参数少）
4. 相关链接
1) 白翔的个人主页：http://mc.eistar.net/~xbai/
2) 论文的下载地址：https://arxiv.org/pdf/1507.05717v1.pdf
3) 代码的下载地址：
http://mc.eistar.net/~xbai/CRNN/crnn_code.zip
5. 论文细节
1) 论文的框架
2) 特征提取层
3) 序列标定层
4) 翻译层
5) 网络训练
6) 实验
7) 总结
8) 问题
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