A method for detecting document orientation by using SVM classifier

Author

Chen, YouGuang ; Guo, Jun ; Deng, Xue ; Zhu, Min

Author_Institution

Comput. Center, East China Normal Univ., Shanghai, China

fYear

2011

fDate

26-28 July 2011

Firstpage

47

Lastpage

50

Abstract

An approach for document orientation detection and classification by using support vector machine (SVM) theorem is proposed in this paper. First, all the characters in a document image will be isolated and some valid ones are selected.Using the valid characters,the document image will be vectorized to a 32 dimensional vector by the feature extracting. By training lots of samples, an SVM classifier can be obtained, and then the orientation of unknown document images can be classified. Experimental results show the accuracy of the proposed method is considerably higher than Bray Curtis distance, even for some bad samples.

Keywords

document image processing; feature extraction; pattern classification; support vector machines; 32-dimensional vector; Bray Curtis distance; SVM classifier; document image; document orientation detection; feature extraction; support vector machine theorem; Accuracy; Feature extraction; Handwriting recognition; Kernel; Support vector machine classification; document orientation detection; feature extract; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia Technology (ICMT), 2011 International Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-61284-771-9

Type

conf

DOI

10.1109/ICMT.2011.6003081

Filename

6003081