基于自产生投票的手写汉字识别

邵允学; 王春恒; 肖柏华; 张琳波

doi:10.3724/SP.J.1004.2013.00450

基于自产生投票的手写汉字识别

doi: 10.3724/SP.J.1004.2013.00450 cstr: 32138.14.SP.J.1004.2013.00450

1.
中国科学院自动化研究所复杂系统管理与控制国家重点实验室北京 100190;
2.
中国交通运输部科学研究院北京 100029

详细信息

通讯作者:
王春恒

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- 被引次数: 0
出版历程
- 收稿日期: 2012-03-09
- 修回日期: 2012-06-15
- 刊出日期: 2013-04-20

Handwritten Chinese Character Recognition Based on Self-generation Voting

1.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190;
2.
China Academy of Transportation Sciences, Beijing 100029

摘要

摘要: 在模式识别领域,投票策略是非常有效的,而且已被成功应用到人脸检测、识别等领域. 然而,在手写汉字识别 (Handwritten Chinese character recognition, HCCR)中,由于类别集很大、训练样本少等特点,现有的很多分类器集成方法方法都很难直接应用于此领域. 本文提出一种自产生式投票的方法,该方法通过事先学习得到的参数集产生一个测试集合,然后用一个分类器去识别测试集合中的每个样本,得到属于各个类别的概率,最后通过加权投票得到识别结果. 实验结果表明,本文提出的方法是实用和有效的.
- 手写汉字识别 /
- 自产生投票 /
- 改进二次判别函数 /
- 线密度归一化
Abstract: Voting strategy is very useful in pattern recognition and it has been successfully applied in many applications like face detection and recognition. However, these state-of-the-art methods are infeasible or unsuitable for handwritten Chinese character recognition (HCCR) because of the problem's characteristics. In this paper, a self-generation voting based method is proposed for further improving the recognition rate in handwritten Chinese character recognition. This method learns a set of parameters first for generating a set of samples from the test sample, and then classify these generated samples using a base-line classifier. At last, it gives the recognition result by voting. Experimental results on two databases show that the proposed method is effective and useful in handwritten Chinese character recognition systems.
- Handwritten Chinese character recognition (HCCR) /
- self-generation voting /
- modified quadratic discriminant function (MQDF) /
- line density equalization

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参考文献(1)

[1]

Yamada H, Yamamoto K, Saito T. A nonlinear normalization method for handprinted Kanji character recognition-line density equalization. Pattern Recognition, 1990, 23(9): 1023-1029[2] Tsukumo J, Tanaka H. Classification of handprinted Chinese characters using nonlinear normalization and correlation methods. In: Proceedings of the 9th International Conference Pattern Recognition. Rome, Italy: IEEE, 1988. 168-171[3] Kimura F, Takashina K, Tsuruoka S, Miyake Y. Modified quadratic discriminant functions and the application to Chinese character recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(1): 149-153[4] Shi M, Fujisawa Y, Wakabayashi T, Kimura F. Handwritten numeral recognition using gradient and curvature of gray scale image. Pattern Recognition, 2002, 35(10): 2051-2059[5] Liu C L, Nakashima K, Sako H, Fujisawa H. Handwritten digit recognition: investigation of normalization and feature extraction techniques. Pattern Recognition, 2004, 37(2): 265-279[6] Gao T F, Liu C L. High accuracy handwritten Chinese character recognition using LDA-based compound distances. Pattern Recognition, 2008, 41(11): 3442-3451[7] Liu C L. High accuracy handwritten Chinese character recognition using quadratic classifiers with discriminative feature extraction. In: Proceedings of the 18th International Conference on Pattern Recognition. Washington DC: IEEE, 2006. 942-945[8] Liu C L, Sako H, Fujisawa H. Handwritten Chinese character recognition: alternatives to nonlinear normalization. In: Proceedings of the 7th International Conference on Document Analysis and Recognition. Edinburgh, UK: IEEE, 2003. 524-528[9] Leung K C, Leung C H. Recognition of handwritten Chinese characters by critical region analysis. Pattern Recognition, 2010, 43(3); 949-961[10] Xu B, Huang K Z, Liu C L. Similar handwritten Chinese characters recognition by critical region selection based on average symmetric uncertainty. In: Proceedings of the 2010 International Conference on Frontiers in Handwriting Recognition. Kolkata, India: IEEE, 2010. 527-532[11] Breiman L. Bagging predictors. Machine Learning, 1996, 24(2): 123-140[12] Leung K C, Leung C H. Recognition of handwritten Chinese characters by combining regularization, Fisher's discriminant and distorted sample generation. In: Proceedings of the 10th International Conference on Document Analysis and Recognition. Barcelona, Spain: IEEE, 2009. 1026-1030[13] Liu C L, Yin F, Wang D H, Wang Q F. CASIA online and offline Chinese handwriting databases. In: Proceedings of the 2011 International Conference on Document Analysis and Recognition. Beijing, China: IEEE, 2011. 37-41[14] Suzuki M, Ohmachi S, Kato N, Aso H, Nemoto Y. A discrimination method of similar characters using compound Mahalanobis function. Trans. IEICE Jpn, 1997, J80-D-II(10): 2752-2760

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