CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
End-to-End Chinese Image Text Recognition with Attention Model
Fenfen Sheng1,2
2017
Conference NameInternational Conference on Neural Information Processing
Conference DateNovember 14-18, 2017
Conference PlaceGuangzhou, China
AbstractThis paper presents an attention-based model for end-to-end
Chinese image text recognition. The proposed model includes an encoder
and a decoder. For each input text image, the encoder part firstly combines deep convolutional layers with bidirectional Recurrent Neural Network to generate an ordered, high-level feature sequence, which could
avoid the complicated text segmentation pre-processing. Then in the
decoder, a recurrent network with attention mechanism is developed to
generate text line output, enabling the model to selectively exploit image
features from the encoder correspondingly. The whole segmentationfree model allows end-to-end training within a standard backpropagation algorithm. Extensive experiments demonstrate significant performance improvements comparing to baseline systems. Furthermore, qualitative analysis reveals that the proposed model could learn the alignment
between input and output in accordance with the intuition.

KeywordChinese Images Text Recognition · End-to-end · Attention · Segmentation-free
Subject AreaComputer Vision
DOI10.1007/978-3-319-70090-8_19
Citation statistics
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19661
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Affiliation1.Institute of AutomationChinese Academy of SciencesBeijingChina
2.University of Chinese Academy of SciencesBeijingChina
Recommended Citation
GB/T 7714
Fenfen Sheng. End-to-End Chinese Image Text Recognition with Attention Model[C],2017.
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