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Characters Segmentation of Cursive Handwritten Words based on Contour Analysis and Neural Network Validation

DOI: 10.5614/itbj.ict.2011.5.1.1

Keywords: character segmentation , contour analysis , neural network validation , unconstrained handwritten word.

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This paper presents a robust algorithm to identify the letter boundaries in images of unconstrained handwritten word. The proposed algorithm is based on vertical contour analysis. Proposed algorithm is performed to generate pre-segmentation by analyzing the vertical contours from right to left. The unwanted segmentation points are reduced using neural network validation to improve accuracy of segmentation. The neural network is utilized to validate segmentation points. The experiments are performed on the IAM benchmark database. The results are showing that the proposed algorithm capable to accurately locating the letter boundaries for unconstrained handwritten words.


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