Cursive script segmentation with neural confidence

This paper presents a new, simple and fast approach for character segmen- tation of unconstrained handwritten words. The proposed approachrst seeks the possible character boundaries based on characters geometric features analysis. However, due to inherited ambiguity and a lack of context, few characters are over-segmented. To increase the efficiency of the proposed approach, an Articial Neural Network is trained with sig- nicant number of valid segmentation points for cursive handwritten words. Trained neural network extracts incorrect segmented points efficiently with high speed. For fair comparison, benchmark database CEDAR is used. The experimental results are promis- ing from complexity and accuracy points of view. Keywords: Handwriting recognition, Character segmentation, Feature extraction, Char- acter recognition, Back propagation learning

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