Iterative cross section sequence graph for handwritten character segmentation
Identifieur interne : 000274 ( PascalFrancis/Checkpoint ); précédent : 000273; suivant : 000275Iterative cross section sequence graph for handwritten character segmentation
Auteurs : Amer Dawoud [Canada]Source :
- IEEE transactions on image processing [ 1057-7149 ] ; 2007.
Descripteurs français
- Pascal (Inist)
- Méthode itérative, Caractère manuscrit, Segmentation, Algorithme, Graphe conceptuel, Détection seuil, Perte information, Image binaire, Congestion trafic, Gestion trafic, Squelette, Extraction caractéristique, Evaluation performance, Reconnaissance optique caractère, Télétrafic, Traitement signal, Reconnaissance forme.
English descriptors
- KwdEn :
- Algorithm, Binary image, Conceptual graph, Feature extraction, Information loss, Iterative method, Manuscript character, Optical character recognition, Pattern recognition, Performance evaluation, Segmentation, Signal processing, Skeleton, Teletraffic, Threshold detection, Traffic congestion, Traffic management.
Abstract
-The iterative cross section sequence graph (ICSSG) is an algorithm for handwritten character segmentation. It expands the cross section sequence graph concept by applying it iteratively at equally spaced thresholds. The iterative thresholding reduces the effect of information loss associated with image binarization. ICSSG preserves the characters' skeletal structure by preventing the interference of pixels that causes flooding of adjacent characters' segments. Improving the structural quality of the characters' skeleton facilitates better feature extraction and classification, which improves the overall performance of optical character recognition (OCR). Experimental results showed significant improvements in OCR recognition rates compared to other well-established segmentation algorithms.
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It expands the cross section sequence graph concept by applying it iteratively at equally spaced thresholds. The iterative thresholding reduces the effect of information loss associated with image binarization. ICSSG preserves the characters' skeletal structure by preventing the interference of pixels that causes flooding of adjacent characters' segments. Improving the structural quality of the characters' skeleton facilitates better feature extraction and classification, which improves the overall performance of optical character recognition (OCR). 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