期刊名称：International Journal of Image Processing (IJIP)
电子版ISSN：1985-2304
出版年度：2010
卷号：4
期号：1
页码：35-52
出版社：Computer Science Journals
摘要：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. In 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. Experimentation conducted involved 1500 text words for learning and 1200 text words for testing. Extensive experimentation has been carried out on both manually created data set and scanned data set. The results are very 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.
关键词：Multi-lingual document processing; Script Identification; Feature Extraction; Binary Tree Classifier
