Cursive Character Segmentation Using Neural Network Techniques

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Blumenstein, Michael
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Simone Marinai and Hiromichi Fujisawa

Date
2008
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548751 bytes

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Abstract

The segmentation of cursive and mixed scripts persists to be a difficult problem in the area of handwriting recognition. This research details advances for segmenting characters in off-line cursive script. Specifically, a heuristic algorithm and a neural network-based technique, which uses a structural feature vector representation, are proposed and combined for identifying incorrect segmentation points. Following the location of appropriate anchorage points, a character extraction technique, using segmentation paths, is employed to complete the segmentation process. Results are presented for neural-based heuristic segmentation, segmentation point validation, character recognition, segmentation path detection and overall segmentation accuracy.

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Machine Learning in Document Analysis and Recognition

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1st

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Pattern Recognition and Data Mining

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