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A multi-font, multi-size Optical Character Recognizer (OCR) of Tamil Script is developed. The input image to the system is binary and is assumed to contain only text. The skew angle of the document is estimated using a combination of Hough transform and Principal Component Analysis. A multi-rate-signal-processing based algorithm is devised to achieve distortion-free rotation of the binary image during skew correction. Text segmentation is noise-tolerant. The statistics of the line height and the character gap are used to segment the text lines and the words. The images of the words are subjected to morphological closing followed by connected component-based segmentation to separate out the individual symbols. Each segmented symbol is resized to a pre-fixed size and thinned before it is fed to the classifier. A three-level, tree-structured classifier for Tamil script is designed. The net classification accuracy is 99.01%. .

Added on September 23, 2014

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  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : A.G. Ramakrishnan, Kaushik Mahata
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