HMM-based Indic handwritten word recognition using zone segmentation. (December 2016)

• Record Type: Journal Article
• Authors: Roy, Partha Pratim; Bhunia, Ayan Kumar; Das, Ayan; Dey, Prasenjit; Pal, Umapada
• Is Part Of: Pattern recognition. Volume 60(2016:Dec.)
• Page Start: 1057
• Page End: 1075
• Publication Date: 2016-12
• Subjects: Handwritten word recognition -- Hidden Markov Model -- Indian script recognition; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.04.012
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: This paper presents a novel approach towards Indic handwritten word recognition using zone-wise information. Because of complex nature due to compound characters, modifiers, overlapping and touching, etc., character segmentation and recognition is a tedious job in Indic scripts (e.g. Devanagari, Bangla, Gurumukhi, and other similar scripts). To avoid character segmentation in such scripts, HMM-based sequence modeling has been used earlier in holistic way. This paper proposes an efficient word recognition framework by segmenting the handwritten word images horizontally into three zones (upper, middle and lower) and then recognize the corresponding zones. The main aim of this zone segmentation approach is to reduce the number of distinct component classes compared to the total number of classes in Indic scripts. As a result, use of this zone segmentation approach enhances the recognition performance of the system. The components in middle zone, where characters are mostly touching, are recognized using HMM. After the recognition of middle zone, HMM based Viterbi forced alignment is applied to mark the left and right boundaries of the characters in the middle zone. Next, the residue components, if any, in upper and lower zones are obtained in a character boundary then the components are combined with the character to achieve the final word level recognition. Water reservoir-based properties have been integrated in this framework to improve the zone segmentation and character boundary detection defects while segmentation. A novel sliding window-based feature, called Pyramid Histogram of Oriented Gradient (PHOG) is proposed for middle zone recognition. PHOG features have been compared with other existing features and found robust for Indic script recognition. An exhaustive experiment is performed on two Indic scripts namely, Bangla and Devanagari for the performance evaluation. From the experiment, it has been noted that proposed zone-wise recognition improves accuracy with respect to the traditional way of Indic word recognition. Highlights: A novel approach of Indic handwritten word recognition using zone segmentation. Efficient PHOG features developed to improve the performance of HMM based middle zone recognition. Integration of water reservoir concept for better character alignment in a word image. A detailed study of experimental results in Bangla and Devanagari scripts has been performed. The proposed framework outperforms traditional without-zone-segmentation based recognition systems.