Radical-Based Chinese Character Recognition via Multi-Labeled Learning of Deep Residual Networks
文献类型：会议论文
| 作者 | Wang TQ(王铁强)1,2 ; Yin F(殷飞)1; Liu CL(刘成林)1,2; Cheng-Lin Liu
|
| 出版日期 | 2017 |
| 会议日期 | November 13-15, 2017 |
| 会议地点 | Kyoto, Japan |
| 关键词 | Chinese Character Recognition Radical Detection Deep Residual Network Multi-labeled Learning |
| 页码 | 579-584 |
| 英文摘要 | The digitization of Chinese historical documents poses a new challenge that in the huge set of character categories, majority of characters are not in common use now and have few samples for training the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. |
| 项目编号 | NSFC-61573355 ; NSFC-61633021 |
| 资助机构 | Osaka Prefecture University, Japan |
| 会议录 | Proc. 14th Int. Conf. Document Analysis and Recognition
 |
| 语种 | 英语 |
| URL标识 | 查看原文 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/19971]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 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]. 见:. Kyoto, Japan. November 13-15, 2017. |
入库方式： OAI收割
来源：自动化研究所
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