摘要
提出一种组合特征作为Bp神经网络输入层向量实现数字字符识别算法.该算法首先引入了数字字符结构特征中图段特征,并结合数字字符的行列统计特征组合成为新的特征向量;然后根据新的组合特征向量设计Bp神经网络分类器;最后对已有的数字图像样本空间中的训练样本库按照Bp神经网络分类器训练方法进行训练,并对测试样本库中的样本进行识别.根据测试实验,数字字符的识别准确率可达到94%以上.
The digital characters recognition method based on the combined feature which is used as input layer feature vector in Bp neural network is proposed. First, segment feature, which is introduced in this paper, and the ranks of the statistical characteristics of the numeric characters are combined as a new feature. Then, Bp neural network classifier is designed according to the combined new feature. Finally, when the training sample set is trained according to the training method of the BP neural network, the recognition results of the test sample set can be acquired. According to the result, the recognition rate can reach at above 94%.
出处
《计算机系统应用》
2013年第3期113-116,54,共5页
Computer Systems & Applications
关键词
组合特征
BP神经网络
分类器
数字识别
图段
combined feature
Bp neural network
classifier
digital recognition
segment feature
