Handwritten Bangla Digit Recognition Using Classifier Combination Through DS Technique
The work presents an application of Dempster-Shafer (DS) technique for combination of classification decisions obtained from two Multi Layer Perceptron (MLP) based classifiers for optical character recognition (OCR) of handwritten Bangla digits using two different feature sets. Bangla is the second most popular script in the Indian subcontinent and the fifth most popular language in the world. The two feature sets used for the work are so designed that they can supply complementary information, at least to some extent, about the classes of digit patterns to the MLP classifiers. On experimentation with a database of 6000 samples, the technique is found to improve recognition performances by a minimum of 1.2% and a maximum of 2.32% compared to the average recognition rate of the individual MLP classifiers after 3-fold cross validation of results. The overall recognition rate as observed for the same is 95.1% on average.
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