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Handwritten Bangla Alphabet Recognition using an MLP Based Classifier
The work presented here involves the design of a Multi Layer Perceptron ...
03/05/2012 ∙ by Subhadip Basu, et al. ∙
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Handwritten Arabic Numeral Recognition using a Multi Layer Perceptron
Handwritten numeral recognition is in general a benchmark problem of Pat...
03/09/2010 ∙ by Nibaran Das, et al. ∙
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Recognition of Offline Handwritten Devanagari Numerals using Regional Weighted Run Length Features
Recognition of handwritten Roman characters and numerals has been extens...
06/29/2018 ∙ by Pawan Kumar Singh, et al. ∙
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An Improved Feature Descriptor for Recognition of Handwritten Bangla Alphabet
Appropriate feature set for representation of pattern classes is one of ...
01/22/2015 ∙ by Nibaran Das, et al. ∙
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A comparative study of different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron
The work presents a comparative assessment of seven different feature se...
03/09/2010 ∙ by Nibaran Das, et al. ∙
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A GA Based approach for selection of local features for recognition of handwritten Bangla numerals
Soft computing approaches are mainly designed to address the real world ...
01/22/2015 ∙ by Nibaran Das, et al. ∙
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A Generative Model for Multi-Dialect Representation
In the era of deep learning several unsupervised models have been develo...
08/17/2015 ∙ by Emmanuel N. Osegi, et al. ∙
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An MLP based Approach for Recognition of Handwritten `Bangla' Numerals
03/05/2012 ∙ by Subhadip Basu, et al. ∙
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The work presented here involves the design of a Multi Layer Perceptron (MLP) based pattern classifier for recognition of handwritten Bangla digits using a 76 element feature vector. Bangla is the second most popular script and language in the Indian subcontinent and the fifth most popular language in the world. The feature set developed for representing handwritten Bangla numerals here includes 24 shadow features, 16 centroid features and 36 longest-run features. On experimentation with a database of 6000 samples, the technique yields an average recognition rate of 96.67 validation of results. It is useful for applications related to OCR of handwritten Bangla Digit and can also be extended to include OCR of handwritten characters of Bangla alphabet.
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