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Optical character recognition refers to the process of translating segmented hand-written images or typewritten images into machine editable text. In t his 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 sys tem in recognizing the palm leaf manuscripts characters and the achieved improvement in characte r 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.

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