Off-Line Cursive Script Word Recognition
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The purpose of this paper is to present a novel contourcode feature in conjunction with a rule basedsegmentation for cursive handwriting recognition. Aheuristic segmentation algorithm is initially used to oversegment each word. Then the prospective segmentationpoints are passed through the rule-based module todiscard the incorrect segmentation points and include anymissing segmentation points. The proposed rule-basedmodule validates every segmentation points againstclosed area, average character size, left character anddensity. During the left char validation, a contour codefeature is extracted and checked weather the left of theprospective segmentation point is a character or rubbish(non-char). The neural network used for this validationwas trained on character and non-character database.Following the segmentation, the contour between correctsegmentation points is passed through the featureextraction module that extracts the contour code, afterwhich another trained neural network is used forclassification. The recognized characters are groupedinto words and passed to a variable length lexicon thatretrieves words that has highest confidence value.