Data Entry using Handwriting Recognition Techniques

• Main Authors: Poo, Hwei Nee, Sebastian, Patrick, Yap, Vooi Voon
• Format: Conference or Workshop Item
• Published: 2007
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utp.edu.my/836/1/Paper_CSPA5016_1-new.pdf; http://eprints.utp.edu.my/836/

The aim of this paper is to use a combination of handwriting recognition and neural network techniques to produce a student coursework database. The proposed method utilizes two cameras to capture the images. Images captured are processed to determine the region of interest (ROI) and to remove noise. Distinctive features from each character are extracted using the combination of five feature extraction modules. The extracted feature matrixes are used as inputs to a Neural Network (NN). The neural network scheme employs the Multi Layer Feed Forward Network as the character classifier. This network is trained using the Back-Propagation algorithm to identify similarities and patterns among different handwriting samples. The system is able to recognize the handwriting of different sizes and styles written using any medium. The system can achieve accuracy rate as high as 88.5% for untrained inputs and 93.83% for trained inputs.