Knowledge Commons of Institute of Automation，CAS
| Discriminative quadratic feature learning for handwritten Chinese character recognition | |
Zhou, Ming-Ke; Zhang, Xu-Yao ; Yin, Fei; Liu, Cheng-Lin
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| 发表期刊 | PATTERN RECOGNITION
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| 2016 | |
| 卷号 | 49期号:1页码:7-18 |
| 文章类型 | Article |
| 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. |
| 关键词 | Handwritten Chinese Character Recognition Discriminative Feature Learning Quadratic Correlation Dimensionality Promotion Training Set Expansion |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1016/j.patcog.2015.07.007 |
| 关键词[WOS] | FEATURE-EXTRACTION ; NUMERAL RECOGNITION ; BENCHMARKING ; DATABASES ; ONLINE ; LINE |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000363077400001 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/10335 |
| 专题 | 多模态人工智能系统全国重点实验室_模式分析与学习 |
| 通讯作者 | Liu, Cheng-Lin |
| 作者单位 | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| 第一作者单位 | 模式识别国家重点实验室 |
| 通讯作者单位 | 模式识别国家重点实验室 |
| 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(1):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(1),7-18. |
| MLA | Zhou, Ming-Ke,et al."Discriminative quadratic feature learning for handwritten Chinese character recognition".PATTERN RECOGNITION 49.1(2016):7-18. |
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| MKZhou2016-PR.pdf（1397KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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