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Le rôle des fréquences spatiales dans l’effet d’encombrement en identification de lettres
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Segment confidence-based binary segmentation (SCBS) for cursive handwritten words
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In: Verma, B & Lee, H 2011, 'Segment confidence-based binary segmentation (SCBS) for cursive handwritten words', Expert Systems with Applications, vol. 38, no. 9, pp.11167-11175, http://dx.doi.org/10.1016/j.eswa.2011.02.162 (2011)
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Abstract:
A novel segment confidence-based binary segmentation (SCBS) for cursive handwritten words is presented in this paper. SCBS is a character segmentation strategy for off-line cursive handwriting recognition. Unlike the approaches in the literature, SCBS is an unordered segmentation approach. SCBS is repetition of binary segmentation and fusion of segment confidence. Each repetition generates only one final segmentation point. The binary segmentation module is a contour tracing algorithm to find a segmentation path to divide a segment into two segments. A set of segments before binary segmentation is called pre-segments, and a set of segments after binary segmentation is called post-segments. SCBS uses over-segmentation technique to generate suspicious segmentation points on pre-segments. On each suspicious segmentation point, binary segmentation is performed and the highest fusion value is recorded. If the highest fusion value is greater than the one of pre-segments, the suspicious segmentation point becomes the final segmentation point for the iteration. If not, no more segmentation is required. Segment confidence is obtained by fusing mean character, lexical and shape confidences. The proposed approach has been evaluated on local and benchmark (CEDAR) databases.
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Keyword:
080108 Neural; 080109 Pattern Recognition and Data Mining; 890202 Application Tools and System Utilities; Applied research; Evolutionary and Fuzzy Computation; Journal Article. Refereed; Neural networks -- Segmentation algorithms -- Handwriting recognition; Optical character recognition devices; Pattern recognition systems; Scholarly Journal; Writing Data processing
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URL: http://hdl.cqu.edu.au/10018/917326
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