基于图像特征提取的脱机手写数字识别方法

周胜明; 张玉叶; 王春歆

光学与光电技术, 2016, 14 (5): 43, 网络出版: 2016-12-23

基于图像特征提取的脱机手写数字识别方法

An Off-Line Handwritten Numeral Recognition Method Combined With the Statistical Characteristics and Structural Features

论文大纲

周胜明 ^1,*张玉叶 ¹王春歆 ²

作者单位

¹ 海军航空工程学院青岛校区, 山东青岛 266041

² 北海舰队, 山东青岛 266001

脱机手写数字识别主分量分析结构特征统计特征 offline handwritten numeral recognition principal component analysis structural features statistical characteristics

摘要

脱机手写数字识别其本质是数字的图像特征匹配问题,所以需要进行手写数字的特征提取,为了准确识别,往往使用较高的特征维数,这就导致识别效率较低。为了提高识别效率,同时为了保持较高的识别率,提出了一种基于图像特征提取的脱机手写数字识别方法。首先利用主分量分析法抽取数字字符图像的统计特征,来降低数字的特征维数,通过对主分量重建模型的误差分析进行数字识别;然后,结合手写数字的笔画结构不稳定的特点,设计并提取数字的宽高比结构特征,进一步比对识别;最后,利用自制训练样本及测试样本库进行仿真实验,数字识别率为96%,识别准确率较高。

Abstract

Off-line handwritten numeral recognition is a pattern recognition problem of the images of ten numbers. In order to improve the recognition efficiency, the character dimension of numbers image should be decreased. As well, in order to improve the recognition veracity, the character mode instability which resulted from different writing styles and habits should be considered. A numbers recognition method which combined with the statistical characteristics and structural features of numbers is proposed in this paper. Firstly, the principal component analysis (PCA) method was adopted to extract statistical characteristics of numeral image. The numeral recognition will be realized through analysis of the reconstruction error of model which reconstructed by the principal components. In order to further determine the type of numeral, the structural features of width to height rate are designed and added to the recognition process. Finally, through experiments on the identification of numeral image, the reliability and accuracy of this method of digital recognition is verified and the recognition rate is 96%.

参考文献

[1] M Hanmandlu, O V Ramana Murthy. Fuzzy model based recognition of handwritten numerals［J］. Pattern Recognition, 2007, 40: 1840-1854.

[2] Cheng-LinLiu, Kazuki Nakashima, HiroshiSako, et al. Handwritten digit recognition: benchmarking of state-of-the-art techniques［J］. Pattern Recognition, 2003, 36(10): 2271-2285.

[3] 王有伟, 刘捷. 手写体数字识别中一种新的倾斜校正的方法［J］. 计算机工程, 2004, 11(30): 128-130.

WANG You-wei, LIU Jie. A new method for slant correction in recognition of handwritten numerals［J］. Computer Engineering, 2004, 11(30): 128-130.

[4] JIANG Wen, LU Chao-yang, LI Jing. Skew correction of handwritten uyghur character based on hough transform［J］. 微型机与应用, 2013, 32(8): 29-31.

姜文, 卢朝阳, 李静. 基于Hough变换的手写体维文字符倾斜校正算法［J］. 微型机与应用, 2013, 32(8): 29-31.

[5] 芮挺, 沈春林, 丁健, 等. 基于主分量分析的手写数字字符识别［J］. 小型微型计算机系统, 2005, 26(2): 289-292.

RUI Ting, SHEN Chun-lin, DING Jian, et al. Handwritten character recognition using principal component analysis［J］. Mine-micro Systems, 2005, 26(2): 289-292.

[6] 张国华, 万钧力. 基于主分量分析法的脱机手写数字识别［J］. 计算机工程, 2007, 9(18): 219-221.

ZHANG Guo-hua, WAN Jun-li. Offline handwritten numeral recognition based on principal component analysis［J］. Computer Engineering, 2007, 9(18): 219-221.

[7] 芮挺, 沈春林, 丁健, 等. 独立分量重建模型的手写数字字符识别［J］. 计算机辅助设计与图形学学报, 2005, 17(3): 455-459.

RUI Ting, SHEN Chun-lin, DING Jian, et al. Handwritten digit character recognition by model reconstruction based on independent component analysis［J］. Journal of Computer Aided Design & Computer Graphics, 2005, 17(3): 455-459.

[8] 范明, 柴玉梅, 昝红英, 译. 统计学习基础-数据挖掘、推理与预测［M］. 北京: 电子工业出版社, 2004: 7-15.

Trevor Hastie, Robert Tibshirani, Jerome Friedman. The elements of statistical learning data mining, inference, and prediction［M］. Beijing: Publishing House of Electonics Industry, 2004: 7-15.

周胜明, 张玉叶, 王春歆. 基于图像特征提取的脱机手写数字识别方法[J]. 光学与光电技术, 2016, 14(5): 43. ZHOU Sheng-ming, ZHANG Yu-ye, WANG Chun-xin. An Off-Line Handwritten Numeral Recognition Method Combined With the Statistical Characteristics and Structural Features[J]. OPTICS & OPTOELECTRONIC TECHNOLOGY, 2016, 14(5): 43.

基于图像特征提取的脱机手写数字识别方法

关于本站 Cookie 的使用提示

全站搜索

基于图像特征提取的脱机手写数字识别方法

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索