CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Handwritten Chinese Text Recognition Using Separable Multi-Dimensional Recurrent Neural Network
Wu YC(吴一超)1,2; Yin F(殷飞)1; Chen Z(陈卓)1,2; Liu CL(刘成林)1,2
2017
Conference Name2017 14th IAPR International Conference on Document Analysis and Recognition
Conference Date2017-11-13
Conference Place日本京都
AbstractThe Long Short-Term Memory Recurrent Neural
Network (LSTM-RNN) has been demonstrated successful in
handwritten text recognition of Western and Arabic scripts. It
is totally segmentation free and can be trained directly from text
line images. However, the application of LSTM-RNNs (including
Multi-Dimensional LSTM-RNN (MDLSTM-RNN)) to Chinese
text recognition has shown limited success, even when training
them with large datasets and using pre-training on datasets
of other languages. In this paper, we propose a handwritten
Chinese text recognition method by using Separable MDLSTMRNN
(SMDLSTM-RNN) modules, which extract contextual information
in various directions, and consume much less computation
efforts and resources compared with the traditional MDLSTMRNN.
Experimental results on the ICDAR-2013 competition
dataset show that the proposed method performs significantly
better than the previous LSTM-based methods, and can compete
with the state-of-the-art systems.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19794
Collection模式识别国家重点实验室_模式分析与学习
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wu YC,Yin F,Chen Z,et al. Handwritten Chinese Text Recognition Using Separable Multi-Dimensional Recurrent Neural Network[C],2017.
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