CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Chinese Image Text Recognition with BLSTM-CTC: A Segmentation-free Method
Zhai, Chuanlei; Chen ZN(陈智能); Li J(李杰); Xu B(徐波)
2016-10
Conference NameThe 7th Chinese Conference on Pattern Recognition(CCPR 2016)
Conference DateNovember 5-7, 2016
Conference PlaceChengdu, China
AbstractThis paper presents BLSTM-CTC (bidirectional LSTM-Connectionist Temporal Classification), a novel scheme to tackle the Chinese image text recognition problem. Different from traditional methods that perform the recognition on the single character level, the input of BLSTM-CTC is an image text composed of a line of characters and the output is a recognized text sequence, where the recognition is carried out on the whole image text level. To train a neural network for this challenging task, we collect over 2 million news titles from which we generate over 1 million noisy image texts, covering almost the vast majority of common Chinese characters. With these training data, a RNN training procedure is conducted to learn the recognizer. We also carry out some adaptations on the neural network to make it suitable for real scenarios. Experiments on text images from 13 TV channels demonstrate the effectiveness of the proposed pipeline. The results all outperform those of a baseline system.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14521
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Zhai, Chuanlei,Chen ZN,Li J,et al. Chinese Image Text Recognition with BLSTM-CTC: A Segmentation-free Method[C],2016.
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