Abstract
The main problem of Bangla (Bengali) and Devanagari handwriting recognition is the shape similarity of characters. There are only a few pieces of work on writer-independent cursive online Indian text recognition, and the shape similarity problem needs more attention from the researchers. To handle the shape similarity problem of cursive characters of Bangla and Devanagari scripts, in this article, we propose a new category of features called ‘sub-stroke-wise relative feature’ (SRF) which are based on relative information of the constituent parts of the handwritten strokes. Relative information among some of the parts within a character can be a distinctive feature as it scales up small dissimilarities and enhances discrimination among similar-looking shapes. Also, contextual anticipatory phenomena are automatically modeled by this type of feature, as it takes into account the influence of previous and forthcoming strokes. We have tested popular state-of-the-art feature sets as well as proposed SRF using various (up to 20,000-word) lexicons and noticed that SRF significantly outperforms the state-of-the-art feature sets for online Bangla and Devanagari cursive word recognition.
- H. El Abed, M. Kherallah, V. Märgner, and A. M. Alimi. 2011. On-line Arabic handwriting recognition competition, ADAB database and participating systems. International Journal on Document Analysis and Recognition 14, 1 (2011), 15--23. Google Scholar
Digital Library
- A. Bharath and S. Madhvanath. 2012. HMM-based lexicon-driven and lexicon-free word recognition for online handwritten Indic scripts. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 4 (2012), 670--682. Google Scholar
Digital Library
- N. Bhattacharya and U. Pal. 2012. Stroke segmentation and recognition from Bangla online handwritten text. In Proceedings of the 13th International Conference on Frontiers in Handwriting Recognition. 736--741. Google Scholar
Digital Library
- N. Bhattacharya, U. Pal, and F. Kimura. 2013. A system for Bangla online handwritten text. In Proceedings of the 12th International Conference on Document Analysis and Recognition. 1367--1371. Google Scholar
Digital Library
- N. Bhattacharya, U. Pal, and P. P. Roy. 2017. Stroke-order normalisation for online Bangla handwriting recognition. In Proceedings of the 14th International Conference on Document Analysis and Recognition. 206--211.Google Scholar
- C. Bishop. 2006. Pattern Recognition and Machine Learning. Springer-Verlag. Google Scholar
Digital Library
- W. Cho, S. W. Lee, and J. H. Kim. 1995. Modeling and recognition of cursive words with hidden Markov models. Pattern Recognition 28, 12 (Dec. 1995), 1941--1953. Google Scholar
Digital Library
- G. A. Fink, S. Vajda, U. Bhattacharya, S. K. Parui, and B. B. Chaudhuri. 2010. Online Bangla word recognition using sub-stroke level features and hidden Markov models. In Proceedings of the 12th International Conference on Frontiers in Handwriting Recognition. 393--398. Google Scholar
Digital Library
- V. Frinken, N. Bhattacharya, and U. Pal. 2014. Design of unsupervised feature extraction system for on-line Bangla handwriting recognition. In Proceedings of the 11th IAPR International Workshop on Document Analysis Systems. 355--359. Google Scholar
Digital Library
- V. Frinken, N. Bhattacharya, S. Uchida, and U. Pal. 2014. Improved BLSTM neural networks for recognition of on-line Bangla complex words. IAPR Joint International Workshops on Statistical Techniques in Pattern Recognition + Structural and Syntactic Pattern Recognition, Lecture Notes in Computer Science, Springer, Berlin, 404--413. Google Scholar
Digital Library
- R. Ghosh and P. P. Roy. 2016. Comparison of zone-features for online Bengali and Devanagari word recognition using HMM. In Proceedings of the 15th International Conference on Frontiers in Handwriting Recognition. 435--440.Google Scholar
- A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, and J. Schmidhuber. 2009. A novel connectionist system for unconstrained handwriting recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence 31, 5 (May 2009), 855--868. Google Scholar
Digital Library
- S. Jaeger, S. Manke, J. Reichert, and A. Waibel. 2001. Online handwriting recognition: The NPen++ recognizer. International Journal on Document Analysis and Recognition 3, 3 (Mar. 2001) 169--180.Google Scholar
Cross Ref
- F. Jelinek. 1994. Statistical Methods for Speech Recognition. MIT Press. Google Scholar
Digital Library
- Y. Katayama, S. Uchida, and H. Sakoe. 2008. A new HMM for on-line character recognition using pen-direction and pen-coordinate features. In Proceedings of the 19th International Conference on Pattern Recognition. 1--4.Google Scholar
- Y.-F. Lv, L.-L. Huang, D.-H. Wang, and C.-L. Liu. 2013. Learning-based candidate segmentation scoring for real-time recognition of online overlaid Chinese handwriting. In Proceedings of the 12th International Conference on Document Analysis and Recognition. 74--78. Google Scholar
Digital Library
- R. Plamondon and S. N. Srihari. 2000. On-line and off-line handwriting recognition: A comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1 (Jan. 2000), 63--84. Google Scholar
Digital Library
- L. Rabiner. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 2 (Feb. 1989), 257--286.Google Scholar
Cross Ref
- J. Schenk and G. Rigoll. 2006. Novel Hybrid NN/hmm modelling techniques for on-line handwriting recognition. In Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition. 619--623.Google Scholar
- K. Y. Wang, R. G. Casey, and F. M. Wahl. 1982. Document Analysis System. IBM J. Res 26, 647--656. Google Scholar
Digital Library
- S. J. Young et al. 1995. The HTK Hidden Markov Model Toolkit Book, Entropic Cambridge Research Laboratory.Google Scholar
Index Terms
Sub-Stroke-Wise Relative Feature for Online Indic Handwriting Recognition
Recommendations
Bangla online handwriting recognition using recurrent neural network architecture
ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image ProcessingRecognition of unconstrained handwritten texts is always a difficult problem, particularly if the style of handwriting is a mixed cursive one. Among various Indian scripts, only Bangla has this additional difficulty of tackling mixed cur-siveness of its ...
Online handwriting recognition systems for Indic and non-Indic scripts: a review
AbstractHandwriting recognition is one of the challenging tasks in the area of pattern recognition and machine learning. Handwriting recognition has two flavors, namely, Offline Handwriting Recognition and Online Handwriting Recognition. Though, ...
HMM-Based Lexicon-Driven and Lexicon-Free Word Recognition for Online Handwritten Indic Scripts
Research for recognizing online handwritten words in Indic scripts is at its early stages when compared to Latin and Oriental scripts. In this paper, we address this problem specifically for two major Indic scripts—Devanagari and Tamil. In contrast to ...






Comments