A Novel Approach of Bangla Handwritten Text Recognition Using HMM

TLDR: A preliminary experiment is performed on a dataset of 10,120 Bangla handwritten words and it is found that the proposed approach outperforms the custom way of HMM based recognition.

Abstract: This paper presents a novel approach for offline Bangla (Bengali) handwritten word recognition by Hidden Markov Model (HMM). Due to the presence of complex features such as headline, vowels, modifiers, etc., character segmentation in Bangla script is not easy. Also, the position of vowels and compound characters make the segmentation task of words into characters very complex. To take care of these problems we propose a novel method considering a zone-wise break up of words and next perform HMM based recognition. In particular, the word image is segmented into 3 zones, upper, middle and lower, respectively. The components in middle zone are modeled using HMM. By this zone segmentation approach we reduce the number of distinct component classes compared to total number of classes in Bangla character set. Once the middle zone portion is recognized, HMM based forced alignment is applied in this zone to mark the boundaries of individual components. The segmentation paths are extended later to other zones. Next, the residue components, if any, in upper and lower zones in their respective boundary are combined to achieve the final word level recognition. We have performed a preliminary experiment on a dataset of 10,120 Bangla handwritten words and found that the proposed approach outperforms the custom way of HMM based recognition.