Research Article Open Access

FEED FORWARD BACK PROPAGATION NEURAL NETWORK BASED CHARACTER RECOGNITION SYSTEM FOR TAMIL PALM LEAF MANUSCRIPTS

J. Ramya1 and B. Parvathavarthini1
  • 1 , India
Journal of Computer Science
Volume 10 No. 4, 2014, 660-670

DOI: https://doi.org/10.3844/jcssp.2014.660.670

Submitted On: 19 October 2013 Published On: 21 December 2013

How to Cite: Ramya, J. & Parvathavarthini, B. (2014). FEED FORWARD BACK PROPAGATION NEURAL NETWORK BASED CHARACTER RECOGNITION SYSTEM FOR TAMIL PALM LEAF MANUSCRIPTS. Journal of Computer Science, 10(4), 660-670. https://doi.org/10.3844/jcssp.2014.660.670

Abstract

Optical character recognition refers to the process of translating segmented hand-written images or typewritten images into machine editable text. In this study, we propose a Tamil palm leaf manuscripts character recognition system using FFBNN technology. First the palm leaf manuscripts characters are segmented by exploiting the sliding window and adaptive histogram calculation. Afterwards, these segmented characters are assembled and then stored in a database. To accomplish the character recognition process, the characters thus stored in the database are learn by Feed Forward Back Propagation Neural Network (FFBNN). Ten set of Tamil palm leaf manuscripts are utilized to evaluate the performance of proposed FFBNN based character recognition system. The results show the effectiveness of proposed character recognition system in recognizing the palm leaf manuscripts characters and the achieved improvement in character recognition shows accuracy of 90%. The performance of the proposed FFBNN based character recognition system is evaluated by changing the number hidden layers for ten set of Tamil palm leaf manuscripts."

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Keywords

  • Segmentation
  • Optical Character Recognition
  • FFBNN
  • Sliding Window
  • Adaptive Histogram Equalization