应用SLLE实现手写体数字识别

杨晓敏; 吴炜; 何小海; 陈默

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应用SLLE实现手写体数字识别

信息科学 | 更新时间：2020-08-12

- 应用SLLE实现手写体数字识别
- Realization of handwritten numeral character recognition by supervised locally linear embedding
- 光学精密工程 2009年17卷第3期页码：641-647
- 作者机构：
  
  四川大学电子信息学院图像信息研究所,四川成都,610064
- 作者简介：
- 基金信息：
  
  教育部科学技术研究重点资助项目(No.107094)()
- DOI：
  中图分类号： TP391.43
- 收稿日期：2008-04-22，
  
  修回日期：2008-07-10，
  
  网络出版日期：2009-03-25，
  
  纸质出版日期：2009-03-25
- 稿件说明：
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杨晓敏, 吴炜, 何小海, 陈默. 应用SLLE实现手写体数字识别[J]. 光学精密工程, 2009,17(3): 641-647

YANG Xiao-min, WU Wei, HE Xiao-hai, CHEN Mo. Realization of handwritten numeral character recognition by supervised locally linear embedding[J]. Editorial Office of Optics and Precision Engineering, 2009,17(3): 641-647
杨晓敏, 吴炜, 何小海, 陈默. 应用SLLE实现手写体数字识别[J]. 光学精密工程, 2009,17(3): 641-647 DOI：

YANG Xiao-min, WU Wei, HE Xiao-hai, CHEN Mo. Realization of handwritten numeral character recognition by supervised locally linear embedding[J]. Editorial Office of Optics and Precision Engineering, 2009,17(3): 641-647 DOI：

摘要

针对在手写字符识别中由于书写习惯和风格的不同而造成的字符模式不稳定问题

提出了一种基于流形学习的手写体数字识别方法。在流形学习非监督的基础上引入了监督信息

从而保证高维到低维的映射在保留流形某些结构的同时也可进一步分离不同类别的流形。算法首先利用基于监督的局部线性嵌入(SLLE)对手写体数字图像进行字符特征的降维

然后再对降维后的特征进行分类识别。对MINST库中手写体数字数据库进行了实验

实验结果表明

利用SLLE降维以后的特征能够有效地区分字符

识别率可达到93.27％;由于具有较好的识别率

能够发现高维空间的低维嵌入流形。

Abstract

In order to improve the instability of handwritten character pattern caused by different writing styles

a novel handwritten numeral character recognition approach based on manifold learning is proposed in this paper.Based on non-supervised manifold learning

a supervised information is induced to the algorithm to ensure the map from high dimension to low dimension to retain some manifold structures and also to seperate different kinds of manifolds. By proposed method

Supervised Locally Linear Embedding (SLLE) algorithm is used to reduce the dimensionality of input feature.Then

the reduced feature is classified by simple classifier.Finally

the proposed algorithm is tested on the characters in MINST character database. The experimental results demonstrate that the method can effectively improve the recognition rate to 93.27% and can provide a new approach to the research of handwritten numeral character recognition.

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Keywords

references

PANG Y W,LIU ZH K,YU N H.A new nonlinear feature extraction method for face recognition[J].Neurocomputing,2006,69:949-953.[2] YEASIN M,BULLOT B.Comparison of linear and non-linear data projection techniques in recognizing universal facial expressions[J].Neural Networks,2005(5):3087-3092.[3] WU Y M,CHAN K L,WANG L.Face recognition based on discriminative manifold learnings[J].Pattern Recognition,2004(4):171-174.[4] 聂祥飞,郭军. 利用Gabor小波变换解决人脸识别中的小样本问题[J]. 光学精密工程,2007,15(6):973-977. NIE X F,GUO J.Solution of small sample size problem in face recognition using Gabor wavelet transform[J].Opt. Precision Eng., 2007,15(6):973-977.(in Chinese)[5] 李粉兰, 唐文彦,段海峰,等. 分数次幂多项式核函数在核直接判别式分析中的应用[J]. 光学精密工程, 2007,15(9):1410-1414. LI F L, TANG W Y,DUAN H F, et al..Application of fractional power polynomial kernel function to kernel direct discriminant analysis[J].Opt. Precision Eng.,2007,15(9):1410-1414. (in Chinese)[6] SOUVENIR R,PLESS R.Manifold clustering .In:Proc of the 21st Int’1 Conf on Computer Vision(ICCV’05),Los Alamitos:IEEE Computer Society Press,2005:648-653.[7] ROWEIS S T.Nonlinear dimensionality reduction by locally linear embedding [J].Science,2000,290(5500):2323-2326.[8] SAUL L K,ROWEIS S T.Think globally,fit locally:unsupervised learning of nonlinear manifolds[J].Journal of Machine Learning Research,2003,4(6):119-155.[9] DICK D R,ROBERTP W.Locally linear embedding for classification .http://www.ph.tn tudelft.nl/~dick/ph 2002 01.pdf,2002-1[10] LAWRENCE K S,SAM T R.An introduction to locally linear embedding .http://www.cs.to ronto/~roweis /lle/,2001-06-10.[11] 徐志节,杨杰,王猛. 一种新的彩色图像降维方法[J]. 上海交通大学学报,2004,38(12):2063-2067. XU ZH J,YANG J,WANG M.A new nonlinear dimensionality reduction for color image .Journal of Shanghai Jiaotong University,2004,38(12):2063-2067.(in Chinese)[12] 吴炜,杨晓敏,陈默,等. 一种新颖的人脸图像超分辩率技术[J]. 光学精密工程,2008,16(5):815-821. WU W,YANG X M,CHEN M, et al.. Novel method of face hallucination[J].Opt. Precision Eng.,2008,16(5):815-821.(in Chinese)[13] DUDA R O, HART P E, STORK D G. Pattern Classification(Second Edition)[M]. New York: John Wiley & Sons, 2001.[14] 姜铮铟,丁晓青. 基于MQDF的英文OCR多模板分类器[J]. 计算机工程.2005,31(15):56-58. JIANG ZH Y,DING X Q.English OCR multi-template classification based on modified quadratic discriminant function[J].Computer Engineering, 2005,31(15):56-58.(in Chinese)[15] 王和勇,郑杰,姚正安,等. 基于聚类和改进距离的LLE方法在数据降维中的应用[J]. 计算机研究与发展.2006,43(8):1485-1490. WANG H Y,ZHENG J,YAO ZH A, et al..Application of dimension reduction on using improved LLE based on clustering[J].Journal of Computer Research and Development ,2006,43(8):1485-1490.(in Chinese)

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