Handwriting Recognition Using Artificial Neural Network

• Main Author: Goh , Siew Yin
• Format: Final Year Project
• Language: English
• Published: Universiti Teknologi Petronas 2004
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://utpedia.utp.edu.my/7924/1/2004%20Bachelor%20-%20Handwriting%20Recognition%20Using%20Artificial%20Neural%20Network.pdf; http://utpedia.utp.edu.my/7924/

Character recognition is one ofthe areas where neural network technology is being widely used. However, a successful neural network application requires efficient implementation of image processing and feature extraction mechanism. This project will demonstrate neural network application in recognition of constrained isolated English uppercase alphabets, from A to Z. The neural network scheme employs the Multi Layer Feed Forward Network as the alphabet classifier. This network is trained using the Back-Propagation algorithm to identify similarities and patterns among different handwriting samples. Meanwhile, the feature extraction scheme applies the combination of five distinct methods. They are Edge Detection, Kirsch Edge Detection, Line Intersection Detection, Alphabet Profile Feature and Modified Alphabet Encoder, while Image Processing involves the process of noise removal from the scanned grayscale image alphabets. Image Proce^.ng makes cue handwriting easier for extraction. In the handwriting recognition system, the neural network will use 30 sets of handwriting samples, each consisting of26 English uppercase as training inputs and to create an automated system to recognize the handwriting alphabets in different sizes and styles. The statistical studies were done on the network to check the ability and performance of the network. This is to improve and modifying the network in order to increase its accuracy and reliability. It was found that feature extraction plays an important role in making the neural recognition system better for a more accurate detection. The handwriting recognition system is then integrated into MATLAB Graphical User Interface (GUI) that users can use very easily while hiding the complexity of the whole mechanism.