On the Performance Improvement of Devanagari Handwritten Character Recognition
The paper is about the application of mini minibatch stochastic gradient descent (SGD) based learning applied to Multilayer Perceptron in the domain of isolated Devanagari handwritten character/numeral recognition. This technique reduces the variance in the estimate of the gradient and often makes b...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2015/193868 |
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| Summary: | The paper is about the application of mini minibatch stochastic gradient descent (SGD) based learning applied to Multilayer Perceptron in the domain of isolated Devanagari handwritten character/numeral recognition. This technique reduces the variance in the estimate of the gradient and often makes better use of the hierarchical memory organization in modern computers. L2-weight decay is added on minibatch SGD to avoid overfitting. The experiments are conducted firstly on the direct pixel intensity values as features. After that, the experiments are performed on the proposed flexible zone based gradient feature extraction algorithm. The results are promising on most of the standard dataset of Devanagari characters/numerals. |
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| ISSN: | 1687-9724 1687-9732 |