Discriminative quadratic feature learning for handwritten Chinese character recognition
文献类型：期刊论文
| 作者 | Zhou, Ming-Ke; Zhang, Xu-Yao ; Yin, Fei; Liu, Cheng-Lin
|
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2016 |
| 卷号 | 49页码:7-18 |
| 关键词 | Handwritten Chinese character recognition Discriminative feature learning Quadratic correlation Dimensionality promotion Training set expansion |
| 英文摘要 | In this paper, we propose a feature learning method for handwritten Chinese character recognition (HCCR), called discriminative quadratic feature learning (DQFL). Based on original gradient direction feature representation, quadratic correlation between features is used to promote the feature dimensionality, then discriminative feature extraction (DFE) is used for dimensionality reduction. By combining dimensionality promotion and reduction, we can learn a much more discriminative and nonlinear feature representation, which can then boost the classification accuracy significantly. For dimensionality promotion, two types of correlation are exploited, namely, statistical correlation and spatial correlation. Statistical correlation is computed on multiple local feature vectors in different regions of the character image; while spatial correlation encodes the dependency between features of two positions. Feature correlation increases the dimensionality by over 40,000. DFE then reduces the dimensionality to less than 300 without losing discriminability. Classification is performed using nearest prototype classifier (NPC), modified quadratic discriminant function (MQDF) and discriminative learning quadratic discriminant function (DLQDF). In experiments on the CASIA-HWDB1.1 standard dataset, the proposed DQFL method improves the test accuracies of NPC, MQDF and DLQDF by 4.94%, 1.83%, and 1.82%, respectively. The test accuracy is further improved by training set expansion. On the ICDAR 2013 Chinese handwriting recognition competition dataset, the proposed DQFLA+DLQDF classifier outperforms the best participating system based on deep convolutional neural network (CNN), while the test speed is much faster. (C) 2015 Elsevier Ltd. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | FEATURE-EXTRACTION ; NUMERAL RECOGNITION ; BENCHMARKING ; DATABASES ; ONLINE ; LINE |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000363077400001 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/10335]  |
| 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 |
| 作者单位 | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Ming-Ke,Zhang, Xu-Yao,Yin, Fei,et al. Discriminative quadratic feature learning for handwritten Chinese character recognition[J]. PATTERN RECOGNITION,2016,49:7-18. |
| APA | Zhou, Ming-Ke,Zhang, Xu-Yao,Yin, Fei,&Liu, Cheng-Lin.(2016).Discriminative quadratic feature learning for handwritten Chinese character recognition.PATTERN RECOGNITION,49,7-18. |
| MLA | Zhou, Ming-Ke,et al."Discriminative quadratic feature learning for handwritten Chinese character recognition".PATTERN RECOGNITION 49(2016):7-18. |
入库方式： OAI收割
来源：自动化研究所
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