Abstract: In order to solve the difficult problem of handwriting postal codes recognition, an improved coarse grid feature extraction approach which simplifies network input of the neural network was introduced. BP neural network based on LM algorithm for network studying was adopted. LM algorithm is an improved Gauss-Newton algorithm. The improved algorithm further enhances precision, stability and studying speed of the network studying through the simplification of the network input. The simulation results show that the algorithm is effective on the handwriting postal codes recognition.

Key words: BP neural network, LM algorithm, Feature extraction, Handwriting postal codes 

摘要： 为解决手写邮政编码识别困难的问题，引入改进的粗网格特征提取方法，对神经网络的网络输入进行简化，并且采用基于LM算法的BP神经网络来进行网络学习。LM算法是一种改进的高斯-牛顿算法，此算法通过简化的网络输入，进一步提高了网络学习的精度、稳定度和学习速度。仿真结果验证了此算法在手写邮政编码识别中的有效性。

关键词: BP神经网络, LM算法, 特征提取, 手写邮政编码