Implicit vs explicit based script segmentation and recognition : a performance comparison on benchmark database

Abstract

This paper compares implicit and explicit based segmentation techniques for off-line cursive handwriting recognition. Firstly, limitations in conventional implicit based approaches are handled by imposing sequence of heuristic rules to locate prospective segmentation points. However, fine segmentation delayed until character recognition. Additionally, for character hypothesis generation, forward and backward strategies were adopted, whereas character verification was lexicon based. Secondly, in explicit based approach, two ANN were employed. One for segmentation point validation forwarded by heuristic segmenter based on character shape analysis and other for recognition. Finally, for character classification, hybrid statistical features were extracted from segmented characters. Techniques are tested and compared on IAM benchmark database with consistent platform