Character Recognition Using Neural Network

Shafii, Syahfinash (2016) Character Recognition Using Neural Network. Project Report. UTeM, Melaka, Malaysia. (Submitted)

Text Character Recognition Using Neural Network 24 Pages.pdf - Submitted Version Download (484kB)

Abstract

Humans have the ability to recognize characters. For example, human can distinguish between different characters and recognize them easily as an A or a B and so on. Therefore, project is intended to develop a neural network system that is able to perform character recognition, particularly English alphabets. Neural network is a system inspired by human brain function; consists of neurons connected in parallel with the ability to learn. A basic design of neural network has input layer, hidden layer, and output layer. The use of neural network can improve the quality of recognition while achieving good performance. A total of 650 samples characters are used with 25 samples of each character. The performance of evaluation is divided to 80% of training and 20% for testing. Scaled conjugate gradient training function is used as this function can perform faster in pattern recognition as well as its small memory requirement. Two training methods are used. The first one is the Gradient Technique with 39 neurons in hidden layer. The second training method is Geometric Feature Extraction with 35 neurons in hidden layer. Gradient Technique and Geometric Feature Extraction; both show an excellent recognition rate of 94.6% and 94.3% respectively. The output of recognized characters is shown in a .txt file.

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