Word level identification of Kannada, Hindi and English scripts from a tri-lingual document Online publication date: Sun, 17-Oct-2010
by M.C. Padma, P.A. Vijaya
International Journal of Computational Vision and Robotics (IJCVR), Vol. 1, No. 2, 2010
Abstract: In a multi script environment, majority of the documents may contain text information printed 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 tri-lingual 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 600 × 600 pixels. The results are very encouraging and prove the efficacy of the proposed model. The average success rate is found to be 98.8% for manually created dataset and 98.5% for dataset constructed from scanned document images.
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