Off-line Handwritten Digits Recongnition Base On PSO-BP Neural Network

Handwritten character recognition are widely researched over the years, but also one of the most successful applications in the field of pattern recognition, generally it can be divided into two categories: on-line handwritten character recognition and optical character recognition (OCR or off-line character recognition). Online identification is to get the user at the time of the actual writing nib location through digital converter, then access to data, but the off-line identification mainly through scanners and digital input data. Among them, the off-line handwritten numeral recognition has become a hot issue in recent years, it has many potential applications research in many fields, such as sorting of letters, financial statements, bank notes, fax paper reading. A lot of researches are done by domestic and foreign scholars in this regards, its methods include principal component analysis of nonlinear PCA algorithm, hidden Markov models, and support vector machines. However, due to changes in handwritten font large figure, there is more difficult to achieve high recognition rate for the traditional identification methods.In this paper, according to characteristics of off-line handwritten numeral recognition the process, off-line handwriting recognition norms model is proposed based on BP neural network, and conducted training and simulation. First of all, through image processing technology for digital equipment or collection of hand-written scanner for pre-processing of digital information, and secondly, to deal with digital information of the image feature extraction, and finally using BP neural network self-study, anti-noise and parallel computing, as well as strong nonlinear mapping ability, set up a random sample of data models and the classifier prediction models, simulation results show the effectiveness of this model.Since the BP algorithm belongs to the local optimization algorithm, the training process in the network is easy to fall into local minimum points, and the impact of network classifier recognition rate, in view of the problem, in this paper, PSO algorithm based on BP artificial neural network pattern recognition method , based on PSO-BP neural network off-line handwriting recognition norms model, and training and simulation studies, through the nonlinear function of the optimization and simulation of the classification of the sample data to compare the PSO algorithm to optimize classification of BP and BP algorithm classifier separate classification results, results show that the optimized PSO classifier has higher prediction accuracy and generalization ability.