Research Article Open Access

An Efficient Neural Network for Recognizing Gestural Hindi Digits

Nidal Fawzi Shilbayeh1, Mohammad Mahmmoud Alwakeel1 and Maisa Mohy Naser2
  • 1 Department of Computer Science, Faculty of Computer and Information Technology, University of Tabuk, Tabuk, Saudi Arabia
  • 2 Department of Computer Science, Faculty of Information Technology, Middle East University, Jordan


Handwritten Hindi digit recognition plays an important role in eastern Arab countries especially in the courtesy amounts of Arab bank checks, recognizing numbers in car plates, or in postal code for mail sorting. 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 been carried out to determine the number of hidden nodes 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 selected 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 other Hindi digit recognition systems especially if we consider the way of writing (mouse and children) in our trained and tested results.

American Journal of Applied Sciences
Volume 10 No. 9, 2013, 938-951


Submitted On: 20 June 2012 Published On: 24 August 2013

How to Cite: Shilbayeh, N. F., Alwakeel, M. M. & Naser, M. M. (2013). An Efficient Neural Network for Recognizing Gestural Hindi Digits. American Journal of Applied Sciences, 10(9), 938-951.

  • 3 Citations



  • Digit Recognition
  • Hindi Digits
  • Mouse Gesture
  • Neural Networks
  • MLP
  • Backpropagation
  • Feature Extraction