Journal of Advances in Engineering Sciences

M. C. Padma ¹, P. A. Vijaya ²

Affiliations
1 Dept. of Computer Science & Engineering, PES College of Engineering, Mandya-571401, Karnataka,, IN
2 Dept. of Electronics & Communication Engg., Malnad College of Engineering, Hassan-573201, Karnataka, IN

Abstract

In a multi script environment, majority of the documents may contain text information in more than one script/language forms. For automatic In a multi script environment, majority of the documents may contain text information in more than one script/ language forms. For automatic processing of such documents through Optical Character Recognition (OCR), it is necessary to identify different script regions of the document. With this context, this paper proposes to develop a model to identify and separate text words of Kannada, Hindi and English scripts from a printed trilingual document. The proposed method is trained to learn thoroughly the distinct features of each script. The binary tree classifier is used to classify the input text image. Experiments were conducted on manually created document images of size 600x600 pixels. The results are encouraging and prove the efficacy of the proposed model. The average success rate is found to be 99% for manually created data set and 98.5% for data set constructed from scanned document images.

Keywords

Multi-Lingual Document Processing, Script Identification, Feature Extraction, Binary Tree Classifier.

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