Analysis and Comparison of Digit Recognition Using Neural Network Approaches

• Main Author: 曾榮鴻
• Other Authors: 莊季高
• Format: Others
• Language: zh-TW
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/60745557877481815921

碩士 === 國立海洋大學 === 航運技術研究所 === 90 === Pattern recognition has been developed for years. The major purpose is to bestow the ability of pattern recognition upon a machine. However, because of the information explosion in recent years, the number of documents that need to be managed has grown rapidly. Because handling documents by hand requires a lot of time and labor, many researchers have applied different artificial intelligence pattern recognition methods to reduce the use of manpower. Among these methods, the neural network is the most widely adopted. However, all of the researches that used the neural network in the field of pattern recognition have involved network structure or learning method improvements. There is no comparison and analysis based on traditional neural network applications to pattern recognition. Neural networks, classified by network structure, could be roughly divided in two categories: feedforward network and recurrent network. This research puts focus on three kinds of neural networks that can be applied to pattern recognition. They are the Backpropagation Network, Probabilistic Neural Network, Radial Basis Function Network, Hopfield Network, Adaptive Resonance Theory Network, and Bidirectional Associative Memory Network. In the digit recognition examples, hazy numerals or cluttered numerals are recognized and compared by these neural networks. The tolerance limit to the clutter and the memory capacity with respect to different neural networks are analyzed. Through the tolerance limit, memory capacity, mathematical calculations, and characteristics of each network, the analysis and the comparison are presented. This study provides another approach for comparison with other researches that focus only on the identification success rate in text recognition.