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• W2395883594 abstract "In a multi script environment, majority of the documents may contain text information printed in more than one script/language. 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, it is proposed to develop a model to identify the script type of a trilingual document printed in Kannada, Hindi and English scripts. The distinct characteristic features of Kannada, Hindi and English scripts are thoroughly studied from the nature of the top and bottom profiles. The proposed model is trained to learn thoroughly the distinct features of each script. Experimentation conducted involved 1500 text lines for learning and 1500 text lines for testing. The k-nearest neighbor classifier is used to classify the test sample. The results are encouraging and prove the efficacy of the proposed model. The average success rate is found to be 99.5% for data set constructed from scanned document images." @default.
• W2395883594 created "2016-06-24" @default.
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• W2395883594 date "2010-01-01" @default.
• W2395883594 modified "2023-09-27" @default.
• W2395883594 title "Script Identification from Trilingual Documents using Profile Based Features." @default.
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