Research Article Open Access

Effect of Hidden Layer Neurons on the Classification of Optical Character Recognition Typed Arabic Numerals

Nidal F. Shilbayeh and Mahmoud Z. Iskandarani

Abstract

Problem statement: The effect of varying the number of nodes in the hidden layer and number of iterations are important factors in the recognition rate. In this paper, a novel and effective criterion based on Cross Pruning (CP) algorithm is proposed to optimize number of hidden neurons and number of iterations in Multi Layer Perceptron (MLP) neural based recognition system. Our technique uses rule-based and neural network pattern recognition methods in an integrated system in order to perform learning and recognition of dynamically printed numerals Approach: The study investigates the effect of varying the size of the network hidden layers (pruning) and number of iterations (epochs) on the classification and performance of the used MLP. The optimum number of hidden neurons and epochs is experimentally established through the use of our novel Cross Pruning (CP) algorithm and via designing special neural based software. The designed software implements sigmoid as its shaping function. Results: Experimental results are presented on the classification accuracy and recognition. Significant recognition rate improvement is achieved using 1000 epochs and 25 hidden neurons in our MLP OCR numeral recognition system. Conclusions/Recommendations: Our approach has a significant improvement in learning and classification of any numeral, character MLP based recognition system.

Journal of Computer Science
Volume 4 No. 7, 2008, 578-584

DOI: https://doi.org/10.3844/jcssp.2008.578.584

Submitted On: 20 August 2008 Published On: 31 July 2008

How to Cite: Shilbayeh, N. F. & Iskandarani, M. Z. (2008). Effect of Hidden Layer Neurons on the Classification of Optical Character Recognition Typed Arabic Numerals . Journal of Computer Science, 4(7), 578-584. https://doi.org/10.3844/jcssp.2008.578.584

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Keywords

  • Neural network
  • MLP
  • pruning
  • pattern recognition
  • classification
  • OCR