Shashikiran, K and Kunwar, Rituraj and Prasad, KS and Ramakrishnan, AG (2010) Comparison of HMM and SDTW for Tamil handwritten character recognition. In: 2010 International Conference on Signal Processing and Communications (SPCOM), 18-21 July 2010 , Bangalore.
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In this paper, we compare the experimental results for Tamil online handwritten character recognition using HMM and Statistical Dynamic Time Warping (SDTW) as classifiers. HMM was used for a 156-class problem. Different feature sets and values for the HMM states & mixtures were tried and the best combination was found to be 16 states & 14 mixtures, giving an accuracy of 85%. The features used in this combination were retained and a SDTW model with 20 states and single Gaussian was used as classifier. Also, the symbol set was increased to include numerals, punctuation marks and special symbols like $, & and #, taking the number of classes to 188. It was found that, with a small addition to the feature set, this simple SDTW classifier performed on par with the more complicated HMM model, giving an accuracy of 84%. Mixture density estimation computations was reduced by 11 times. The recognition is writer independent, as the dataset used is quite large, with a variety of handwriting styles.
|Item Type:||Conference Paper|
|Additional Information:||Copyright 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Department/Centre:||Division of Electrical Sciences > Electrical Engineering|
|Date Deposited:||29 Dec 2011 07:57|
|Last Modified:||29 Dec 2011 07:57|
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