BP神经网络在图像字符识别中的改进和应用

Improvement and application of BP neural network in image character recognition

传统BP神经网络算法虽然具有良好的学习能力和容错能力,但是收敛速度慢,易陷入局部极小点等缺点制约了它的进一步发展和应用.针对这些不足,采用自适应学习率结合附加动量因子的方法可以有效缩短训练时间,加快收敛速度,同时抑制寻优算法陷入局部极小点.将该算法应用于图像字符识别系统中,通过一系列实验优化系统参数之后给出系统识别结果,表明该系统识别具有较高的准确性和鲁棒性.

The traditional BP neural network algorithm is good in learning ability and fault tolerance,while its dis- advantages such as slow convergence rate and easily falling into local minimum restrict its further development and application. An improved BP algorithm with self-adaptive learning rate and additional momentum factors can effec- tively reduce the training time, speed up the convergence rate and inhibit the possibility of falling into a local mini- mum. The improved algorithm is applied to the image character recognition system. The influences of model parame- ters on performance of BP neural network are analyzed, and the recognition results are given after a series of parame- ter optimization. The experimental results show that the improved BP neural network can recognize image characters with high accuracy and robustness.