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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Journal of the ACM (JACM)
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Touching numeral segmentation using water reservoir concept
Pattern Recognition Letters
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ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
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Pattern Recognition Letters
Filtering segmentation cuts for digit string recognition
Pattern Recognition
Recognition of Multi-oriented Touching Characters in Graphical Documents
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Multi-Oriented and Multi-Sized Touching Character Segmentation Using Dynamic Programming
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Touching String Segmentation Using MRF
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 02
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Proceeding of the workshop on Document Analysis and Recognition
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The touching character segmentation problem becomes complex when touching strings are multi-oriented. Moreover in graphical documents sometimes characters in a single-touching string have different orientations. Segmentation of such complex touching is more challenging. In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region in the background portion. Based on the convex hull information, at first, we use this background information to find some initial points for segmentation of a touching string into possible primitives (a primitive consists of a single character or part of a character). Next, the primitives are merged to get optimum segmentation. A dynamic programming algorithm is applied for this purpose using the total likelihood of characters as the objective function. A SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Experiments were performed in different databases of real and synthetic touching characters and the results show that the method is efficient in segmenting touching characters of arbitrary orientations and sizes.