Stroke-Order Normalization for Online Bangla Handwriting Recognition

Abstract

Stroke order variation within characters is one of the difficult problems in online Bangla handwriting recognition. Moreover, in Bangla, character parts are written in zone-wise manner. Character parts written in the middle zone are generally cursive, while character parts in the upper and lower zones are written using delayed strokes. As online recognition depends on the order of writing, words written with different stroke-order are treated as different words to the online recognizer. To the best of our knowledge, no work has been reported on stroke-order normalization for any Indic script though it is an important aspect of online recognition. In this paper, we propose a stroke-order normalization method for Bangla online recognition using offline and online information. Here, at first, based on the offline information, sub-strokes in a word are ordered according to their relative positions. This results in similar stroke-order among the different instances of the same word. Next, online information of each ordered sub-stroke is used for feature extraction. This normalization approach has several significant advantages, e.g. (i) characters/words having any stroke order can be recognized, (ii) number of word classes is reduced, etc. We have tested our method on a dataset of 6000 words and obtained 74.65% and 90.53% word recognition accuracies, respectively, before and after stroke-order normalization. Thus, stroke-order normalization has enhanced the recognition result drastically (15.88%). © 2017 IEEE.