Handwritten Chinese character recognition by joint classification and similarity ranking
文献类型：会议论文
| 作者 | Cheng Cheng1; Xu-Yao Zhang2 ; Xiao-Hu Shao1; Xiang-Dong Zhou1
|
| 出版日期 | 2016 |
| 会议日期 | October 23-26 |
| 会议地点 | Shenzhen, China |
| 关键词 | Hccr |
| 英文摘要 |
Deep convolutional neural networks (DCNN) have
recently achieved state-of-the-art performance on handwritten
Chinese character recognition (HCCR). However, most of DCNN
models employ the softmax activation function and minimize
cross-entropy loss, which may loss some inter-class information.
To cope with this problem, we demonstrate a small but consistent
advantage of using both classification and similarity ranking
signals as supervision. Specifically, the presented method learns a
DCNN model by maximizing the inter-class variations and minimizing
the intra-class variations, and simultaneously minimizing
the cross-entropy loss. In addition, we also review some loss
functions for similarity ranking and evaluate their performance.
Our experiments demonstrate that the presented method achieves
state-of-the-art accuracy on the well-known ICDAR 2013 offline
HCCR competition dataset. |
| 会议录 | International Conference on Frontiers in Handwriting Recognition (ICFHR)
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12470]  |
| 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 Cheng |
| 作者单位 | 1.重庆绿色科学研究院 2.中科院自动化所 |
| 推荐引用方式 GB/T 7714 | Cheng Cheng,Xu-Yao Zhang,Xiao-Hu Shao,et al. Handwritten Chinese character recognition by joint classification and similarity ranking[C]. 见:. Shenzhen, China. October 23-26. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。