The Intersection Of Neural Network Identification And Preliminary Study On Probabilistic Neural Network For Online Handwritten Character Recognition

The neural network has a history of over fifty years now. Generations of researchers have been making great efforts to build up its theoretical foundation and to apply it in many areas such as signal processing, machine vision, pattern recognition, expert system, industry control and weather forecast. In recent years, the pattern recognition based on neural networks has become a new active field. The study of the neural networks-based pattern recognition system is very important, not only to the development of neural networks theories, but also to the application of the pattern recognition techniques.The multi-layer feedforward back-propagation neural network has found a widely expanding range of applications. It has been used extensively for image and character recognition.The SOFM proposed by Kohonen is also used in the area of pattern recognition for its strong organizing ability on topology and its robustness.Since HMM was introduced at the end of 1960, it has been applied to the connected, speaker-independent, automatic speech recognition with the advantage of modeling various patterns. Recently it has also been widely adapted to character recognition.The work we have done is mainly focused on the following two problems.The first one is junction recognition using neural networks. Combining SOFM network and BP network we construct a multi-classifier to recognize junctions in images. When noises up to 8% are added, the system can still achieve a high recognition ratio of around 85%.Secondly, we use the HMM neural network to recognize on-line handwritten character. With the developing of computer technique, people can process data quickly. But the speed of typing data into computer is lower than the speed of processing data. It's precluding people from using the computer. Furthermore, input with the keyboard would break thinking. So it's necessary to find a convenient way of inputting. By this time, the rate of single handwritten character recognition, especially figure recognition, has reached 95%. But to the sequential characters, or mathematic character recognition there is not a perfect product. We recognize on-line handwritten figure and some mathematic characters using the HMM neural networks, and achieve a better result.Compared with other people's work, we focus on recognizing characters with combination of HMM and neural networks. It makes use of the good performance of neural networks, and avoid the limitation that neural networks are not good at performing the real-time work. Due to the limitation of the characteristic selected and the algorithms used, the model we proposed still needs further improvement for practical application. But the results have shown that the HMM neural network can work successfully on the recognition of handwritten characters.