摘要
为了解决手写体签名识别中数据预处理复杂、分割困难、特征提取不充分等问题,提出了基于小波包分解与高斯模型的脱机手写体签名识别方法。它对归一化的整个签名图像进行小波包分解,对分解值自动聚类和高斯建模,不但没有去噪、旋转、平移、分割的过程,而且特征提取完全且是可逆的分解过程。实验表明提出方法比其他方法具有更好的抗噪性、鲁棒性、适应性和识别率,为含噪脱机手写体签名识别提供了一种可行的技术解决方案。
The paper proposes a way of off-line handwritten signature recognition based on wavelet packs and Gauss model in order to solve the problems of complicated data preprocessing,difficult segmentation and deficient feature extraction.It decomposes the whole signature image with wavelet packs after normalized,clusters the decomposed values and gains Gauss modeling.It doesn＇t need noise reduction,rotation,translation and segmentation,and feature extraction is complete and is reversible decomposed course.The results show that the method proposed has better antinoise,flexibility than other methods and improves the recognition rate.It provides a feasibility techno-project for off-line handwritten signature.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第36期161-164,共4页
Computer Engineering and Applications
基金
中国民航总局项目(NoMHRD200806)
中国民航大学科研基金(No06kym02
No07kym05)
关键词
小波包
高斯模型
标准化
聚类
wavelet packs Guass model normalization clustering
