Development of unconstrained handwritten digit extraction, segmentation and recognition on bank cheques using artificial neural network - Anak Francis, Adam (Faculty of Information Technology and Quantitative Sciences, 2005)
This project is about the handwritten numerical strings that were extracted, segmented, and verified for bank cheques. This project has four objectives. The first objective is to make data collection for digitized handwritten courtesy amount on bank cheques. The second objective is to locate the position of the amount courtesy block for the extraction process by using the Coordinate Search. The third objective is to perform Vertical Splitting Algorithm technique for digit segmentation. And lastly, to develop an Artificial Neural Network for digit recognition. The project is hoped to bring benefits to the people who is doing the same studies on image processing. The general result for this project is that; this system has an accuracy of 60% in recognizing and verifying the handwritten numerical strings for 300 training data sets and 50 testing data sets. For the back- propagation neural network module, the numbers of hidden nodes in the hidden layer that have been selected was 2, the sum squared errors was 0.001, the momentum was 0.95, the learning rate was 0.7 and the initial weight was set in the range of [-2.4, 2.4].