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- W2076243012 abstract "Handwritten Hindi digit recognition plays an import ant role in eastern Arab countries especially in th e courtesy amounts of Arab bank checks, recognizing numbers in car plates, or in postal code for mail so rting. In our study, we proposed an efficient Hindi Digit Recognition System drawn by the mouse and developed using Multilayer Perceptron Neural Network (MLP) with backpropagation. The system has been designed, implemented and tested successfully. Analysis has b een carried out to determine the number of hidden n odes that achieves high performance. The proposed system has been trained on samples of 800 images and tested on samples of 300 images written by different users se lected from different ages. An experimental result shows high accuracy of about 91% on the testing samples and very close to 100% on the training samples. Experiments showed that our result is high in comparison with o ther Hindi digit recognition systems especially if we consider the way of writing (mouse and children) in our trai ned and tested results." @default.
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- W2076243012 date "2013-09-01" @default.
- W2076243012 modified "2023-09-30" @default.
- W2076243012 title "AN EFFICIENT NEURAL NETWORK FOR RECOGNIZING GESTURAL HINDI DIGITS" @default.
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- W2076243012 doi "https://doi.org/10.3844/ajassp.2013.938.951" @default.
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