Tapan Kumar Bhowmik

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A novel hidden Markov model (HMM) for recognition of handwritten Oriya numerals is proposed. The novelty lies in the fact that the HMM states are not determined a priori, but are determined automatically based on a database of handwritten numeral images. A handwritten numeral is assumed to be a string of several shape primitives. These are in fact the(More)
We propose support vector machine (SVM) based hierarchical classification schemes for recognition of handwritten Bangla characters. A comparative study is made among multilayer perceptron, radial basis function network and SVM classifier for this 45 class recognition problem. SVM classifier is found to outperform the other classifiers. A fusion scheme using(More)
Character segmentation is a necessary preprocessing step for character recognition in many handwritten word recognition systems. The most difficult case in character segmentation is the cursive script. Fully cursive nature of Bangla handwriting, the natural skewness in words poses some challenges for automatic character segmentation. In this article a novel(More)
This paper presents a recognition system for isolated handwritten Bangla words, with a fixed lexicon, using a left-right Hidden Markov Model (HMM). A stochastic search method, namely, Genetic Algorithm (GA) is used to train the HMM. A new shape based direction encoding features has been developed and introduced in our recognition system. Both(More)
This paper investigates the performance of hidden Markov models (HMMs) for handwriting recognition. The Segmental K-Means algorithm is used for updating the transition and observation probabilities , instead of the Baum-Welch algorithm. Observation probabilities are modelled as multi-variate Gaussian mixture distributions. A determinis-tic clustering(More)