A New Block Partitioned Text Feature for Text Verification

Author

Wang, Xiufei ; Huang, Lei ; Liu, Changping

Author_Institution

Inst. of Autom., Chinese Acad. of Sci..China, China

fYear

2009

Firstpage

366

Lastpage

370

Abstract

In this paper, a new feature for text verification is proposed. The difficulties for the selection of features for text verification (FTV) are first discussed, followed by two principles for the FTV: the FTV should minimize the influence of backgrounds, and it should also be expressive enough for all the texts varied in structures prominently. In this paper, we exploit different block partition methods and introduce two widely used features: the gray scale contrast (GSC) feature to eliminate the background difference, and the edge orient histogram (EOH) feature to distinguish the structure of texts from that of non-texts. A texture classifier can be got by SVM training of pre-labeled data. The candidate text lines can be verified by this classifier. Experimental results show that our feature performs well.

Keywords

pattern classification; support vector machines; text analysis; SVM training; block partition method; edge orient histogram feature; gray scale contrast feature; pre-labeled data; support vector machine; text verification; texture classifier; Automation; Flowcharts; Histograms; Learning systems; Pattern classification; Support vector machine classification; Support vector machines; Text analysis; Text recognition; Videos;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 2009. ICDAR '09. 10th International Conference on

Conference_Location

Barcelona

ISSN

1520-5363

Print_ISBN

978-1-4244-4500-4

Electronic_ISBN

1520-5363

Type

conf

DOI

10.1109/ICDAR.2009.61

Filename

5277668