Radical-Based Chinese Character Recognition via Multi-Labeled Learning of Deep Residual Networks
Wang TQ(王铁强)1,2; Yin F(殷飞)1; Liu CL(刘成林)1,2; Cheng-Lin Liu
2017
会议名称The 14th IAPR International Conference on Document Analysis and Recognition
会议录名称Proc. 14th Int. Conf. Document Analysis and Recognition
页码579-584
会议日期November 13-15, 2017
会议地点Kyoto, Japan
所属项目编号NSFC-61573355 ; NSFC-61633021
会议主办者Osaka Prefecture University, Japan
摘要The digitization of Chinese historical documents poses a new challenge that in the huge set of character categories, majority of characters are not in common use now and have few samples for training the character classifiers. To settle this problem, we consider the radical-level composition of Chinese characters, and propose to detect position-dependent radicals using a deep residual network with multi-labeled learning. This enables the recognition of novel characters without training samples if the characters are composed of radicals appearing in training samples. In multi-labeled learning, each training character sample is labeled as positive for each radical it contains, such that after training, all the radicals appearing in the character can be detected. Experimental results on a large-category-set database of printed Chinese characters demonstrate that the proposed method can detect radicals accurately. Moreover, according to radical configurations, our model can credibly recognize novel characters as well as trained characters.
关键词Chinese Character Recognition Radical Detection Deep Residual Network Multi-labeled Learning
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收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/19971
专题模式识别国家重点实验室_模式分析与学习
通讯作者Cheng-Lin Liu
作者单位1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Wang TQ,Yin F,Liu CL,et al. Radical-Based Chinese Character Recognition via Multi-Labeled Learning of Deep Residual Networks[C],2017:579-584.
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