On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Role of Holistic Paradigms in Handwritten Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of Elastic Matching Algorithms for Online Tamil Handwritten Character Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Direction Code Based Features for Recognition of Online Handwritten Characters of Bangla
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
On-line Handwriting Recognition of Indian Scripts - The First Benchmark
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
Online Bangla Word Recognition Using Sub-Stroke Level Features and Hidden Markov Models
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
A Hybrid Model for Recognition of Online Handwriting in Indian Scripts
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
Unconstrained Bangla online handwriting recognition based on MLP and SVM
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
HMM-Based Lexicon-Driven and Lexicon-Free Word Recognition for Online Handwritten Indic Scripts
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The rapid spread of pen-based digital devices and touch screen devices coupled with their affordability, and capability to take technology and digitization of data to the grassroots, has made online handwriting recognition an active field of research. The relevance of research on on-line handwriting recognition for Indian scripts is particularly high because the challenges posed by Indian scripts are different from other scripts. This is not only because of their extremely large alphabet size but also because the inter class variability among several classes is very small. In this article, we introduce a limited vocabulary online unconstrained handwritten Bangla (a major Indian script) word recognizer based on a novel word level feature representation. Here, we consider three different features extracted from a word sample and three event strings are generated corresponding to these three features. A distance function is formulated which uses the Levenshtein distance metric to compute the distance between two triplets of event strings representing two word samples. The nearest neighbour scheme is used to classify the input sample. We have simulated the proposed approach on vocabularies of varying sizes and the recognition performances are encouraging.