One of the most classical applications of the Artificial Neural Network is the character recognition system. This system is the base for many different types of applications in various fields, many of which are used in daily lives. Cost effective and less time consuming, businesses, post offices, banks, security systems, and even the field of robotics employ this system as the base of their operations. For character recognition, there are many prosperous algorithms for training neural networks. Back propagation (BP) is the most popular algorithm for supervised training multilayer neural networks. In this thesis, Back propagation (BP) algorithm is implemented for the training of multilayer neural networks employing in character recognition system. The neural network architecture used in this implementation is a fully connected three layer network. The network can train over 16 characters since the 4-element output vector is used as output units. This thesis also evaluates the performance of Back propagation (BP) algorithm with various learning rates and mean square errors. MATLAB Programming language is used for implementation.
Artificial Neural Network, Back propagation, Character Recognition
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