基于Gabor-统计特征与SVM的文档图像文本检测方法  

A Text Detection Approach Based on Gabor-statistical Feature and SVM

作　　者：刘权[1] 苏海[1] 苗敏婧 

机构地区：[1]武汉大学,武汉430079

出　　处：《包装工程》2014年第23期100-103,114,共5页Packaging Engineering

基　　金：国家科技支撑计划资助项目(2013BAH03B01);广东省教育部产学研结合重大专项(2012A090300017)

摘　　要：目的对文档图像中的文本进行精确检测,深入研究统计特征对于文字纹理特征分类的影响。方法首先结合Gabor-统计特征获得文档图像的特征图像,再应用SCA算法提取文本样本和非文本样本,最后采用SVM实现文本检测,而统计特征的选择使用Fisher准则实现。结果依据Fisher准则,逆差距特征对于Gabor特征分类的类间离散度最大,效果最佳。结论针对不同类型的文档图像,使用Gabor-逆差距特征能够获得较好的检测效果。Objective To further improve the quality of document image text detection, in-depth research was performed to analyze how statistical features influenced the classification of text texture. Methods First, the document images＇ feature images were obtained through Gabor-statistical feature, and then the SCA algorithm was applied to extract the text and non-text samples. Finally, SVM was employed to fulfill the text detection. To choose the statistical feature,Fisher criteria were used. Results The experiments implied that homogeneity returned the maximum class separation distance according to Fisher criteria and gave the best detection result. Conclusion A relatively good detection result could be obtained using Gabor-homogeneity feature when dealing with different types of document images.

关 键 词：文档图像 Gabor-统计特征 SVM 文本检测 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]