From contours to characters segmentation of cursive handwritten words with neural assistance

This paper presents a novel algorithm to resolve an open problem to correctly locating letter boundaries in off-line unconstrained cursive handwritten word images. The proposed algorithm is based on vertical contour analysis. Following preprocessing, during the course of pre-segmentation vertical contours are analyzed from right to left. Furthermore to improve accuracy of segmentation, trained ANN is employed to validate segment points. For fair analysis, experiments were performed on IAM benchmark database. Results obtained thus show that the proposed approach is capable to accurately locating the letter boundaries for unconstraint cursive handwritten words.