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：53-65
出版社：Computer Science Journals
摘要：In a multi script environment, an archive of documents having the text regions printed in different scripts is in practice. For automatic processing of such documents through Optical Character Recognition (OCR), it is necessary to identify different script regions of the document. In this paper, a novel texture-based approach is presented to identify the script type of the collection of documents printed in seven scripts, to categorize them for further processing. The South Indian documents printed in the seven scripts - Kannada, Tamil, Telugu, Malayalam, Urdu, Hindi and English are considered here. The document images are decomposed through the Wavelet Packet Decomposition using the Haar basis function up to level two. The texture features are extracted from the sub bands of the wavelet packet decomposition. The Shannon entropy value is computed for the set of sub bands and these entropy values are combined to use as the texture features. Experimentation conducted involved 2100 text images for learning and 1400 text images for testing. Script classification performance is analyzed using the K-nearest neighbor classifier. The average success rate is found to be 99.68%.
