Neocognitron based handwriting recognition system performance tuning using genetic algorithm

Neural networks have been used to recognize handwritten characters such as Chinese, English or numerals. But their performance, i.e., the recognition rate, depends on a number of factors which may include the network architecture, feature selection, network parameter setting, learning strategy, learning sample selection, test pattern preprocessing, etc. These factors are important to network engineer in designing a network for a particular application problem, but unfortunately there is a lack of systematic way to guide their decision making regarding the selection of these parameters. This paper presents a parameter tuning (namely the selectivity parameter) methodology based on a sensitivity analysis of the Neocognitron model, and the off-line handwritten numeral recognition with supervised learning is chosen to be the demonstrated application problem. Genetic algorithm (GA) is used to select parameters leading to improved recognition results. We used a set of training pattern provided by Fukushima as our training patterns which involved no preprocessing, and our experimental results show a significant improvement in performance. A brief discussion on alternate hybrid architecture involving neural network and genetic algorithm, and different fitting functions for the GA will be presented.