DIMENSION REDUCTION FOR SCRIPT CLASSIFICATION- PRINTED INDIAN DOCUMENTS

 

DIMENSION REDUCTION FOR SCRIPT CLASSIFICATION- PRINTED INDIAN DOCUMENTS

 Hamsaveni L1 , Pradeep C2 and Chethan H K3

1Department of Studies in Computer Science Manasagangotri, University of Mysore, Mysore

2Department of Computer Science and Engineering Rajarajeswari College of Engineering, Bangalore

3Department of Computer Science and Engineering Maharaja Institute of Technology, Mysore

 

ABSTRACT

 Automatic identification of a script in a given document image facilitates many important applications such as automatic archiving of multilingual documents, searching online archives of document images and for the selection of script specific OCR in a multilingual environment. This paper provides a comparison study of three dimension reduction techniques, namely partial least squares (PLS), sliced inverse regression (SIR) and principal component analysis (PCA), and evaluates the relative performance of classification procedures incorporating those methods. For given script we extracted different features like Gray Level Co-occurrence Method (GLCM) and Scale invariant feature transform (SIFT) features. The features are extracted globally from a given text block which does not require any complex and reliable segmentation of the document image into lines and characters. Extracted features are reduced using various dimension reduction techniques. The reduced features are fed into Nearest Neighbor classifier. Thus the proposed scheme is efficient and can be used for many practical applications which require processing large volumes of data. The scheme has been tested on 10 Indian scripts and found to be robust in the process of scanning and relatively insensitive to change in font size. This proposed system achieves good classification accuracy on a large testing data set.

 KEYWORDS

 SIFT, GLCM, PLS, SIR, PCA, Nearest Neighbour

https://aircconline.com/ijait/V7N3/7317ijait01.pdf