A novel segmentation technique for online handwritten Bangla words

摘要

In the present work, we have proposed a novel Bangla word segmentation technique that is based on stroke-level busy zone formation procedure. In an unconstrained domain, people often write text where strokes may be poorly aligned (due to multi-directional skewness) and varied combination of strokes with various types of joining between them are possible while forming the words. Hence, a segmentation approach for stroke extraction is pertinent for any stroke-based word recognition system. The presence of a large volume of symbols set (58 basic symbols with more than 280 compound characters) in Bangla script makes the task more challenging. In the current experiment, our stroke-level segmentation approach effectively handles such type of Bangla words. A sub-zoning scheme within busy zone followed by a modified Down-Up-Down (DUD) concept within these sub-zones has been used to find valid segmentation points. This scheme avoids over and under-segmentation issues caused by either inherent writing pattern or due to writing style variations up to certain extent. The proposed segmentation approach has been tested on 6500 online handwritten Bangla word samples with 98.45% correct segmentation accuracy (compared with manually generated ground truth of the same database). (C) 2018 Elsevier B.V. All rights reserved.