基于支持向量机的高棉印刷字符集识别分类器

Pongsametrey Sok, Nguonly Taing
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引用次数: 9

摘要

本文介绍了基于支持向量机(SVM)的分类方法在位图文档高棉印刷字符集识别(PCR)中的应用。高棉语被认为是最复杂的语言之一,共有74个字母,措辞复合可以有多达5个垂直层次。本文提出了一种新的方法——支持向量机(SVM)用于高棉语字符分类系统,通过使用3种不同的支持向量机核(高斯核、多项式核和线性核)进行数据训练和识别,找出高棉语的最佳核。该方法允许我们通过训练不同的字符训练片段来使用小的训练数据集,而不是训练大量的聚类。分类使用二进制数据0作为字符的空白区域,1作为字符的黑色像素区域;每个训练字符块被拉伸成各种图像大小的二进制数据矩阵。从矩阵中提取特征提取用于支持向量机分类。识别后,通过使用字符级别或常见错误纠错来组合每个聚类或字符。对750个高棉纯词或3000个左右字符的实验表明,高斯核支持向量机方法取得了较好的结果,在所有核中都有较好的性能。系统使用字号为32pt的训练数据中的一种字体“Khmer OS Content”来识别3种不同的字体大小。字号28pt的准确率为98.17%,字号32pt的准确率为98.62%,字号36pt的准确率为98.54%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Support Vector Machine (SVM) based classifier for Khmer Printed Character-set Recognition
This paper describes on the use of Support Vector Machine (SVM) based classification method on Khmer Printed Character-set Recognition (PCR) in bitmap document. Khmer language has been identified as one of the most complex language with the total of 74 alphabets and the wording compound can has up to 5 vertical levels. This paper proposes one new method, SVM for Khmer character classification system by using 3 different SVM kernels (Gaussian, Polynomial and Linear Kernel) on data training and recognition to find out the best kernel for Khmer language. The method allows us to use small training dataset by training different pieces of character training instead of training big amount of clusters. The classification uses binary data of 0 as white space and 1 as black pixel area of the character; each training piece of character has been stretched into a matrix of the binary data in all kinds of image size. Feature extraction is extracted from the matrix to use in SVM classification. After recognition, there are some rules to combine each cluster or character by using character levels or common mistake correction. The experiment of about pure 750 Khmer words or around 3000 characters show that SVM method with Gaussian Kernel produces a good result with better performance among all kernels. The system uses one font "Khmer OS Content" of the training data with font size 32pt to recognize 3 different font sizes. The accuracy of 28pt font size is 98.17%, 32pt is 98.62% and 36pt is 98.54% respectively.
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