【作者】 张燕；

【导师】 于明；

【作者基本信息】 河北工业大学 ， 模式识别与智能系统， 2007， 硕士

【摘要】 手写体笔迹鉴别是计算机视觉和模式识别领域中活跃的研究课题之一,它是通过分析不同人书写同一单字或整体的书写风格来判断书写人身份的一门技术。传统的笔迹鉴别方法大多对一段整体文字笔迹的纹理图像提取特征,为提高笔迹图像的鉴别率,本文将非负矩阵分解(NMF)算法运用到离线手写单字的笔迹鉴别中。在应用非负矩阵分解算法前,首先随机初始化两个非负矩阵,为了提高算法的稳定性,本文采用将高维空间中的初始化值应用到低维空间中的方法。从一段文字中选取有代表性的单字应用非负矩阵分解算法,得到单字笔迹图像的特征子空间和特征值。将测试样本影射到特征子空间,得出特征向量,求其和测试样本特征值之间角度的相关性和k近邻,进而对笔迹图像分类。左右结构,笔画复杂的单字识别率较高。实验结果表明,非负矩阵分解算法其分类正确率明显高于传统的主分量分析(PCA)方法。这说明NMF算法在手写笔迹鉴别分析中的潜力。更多还原

【Abstract】 Handwriting identification is one of the popular research subjects in the area of computer vision and pattern recognition. It aims to judge the identity of a writer by comparing the same character written by different people and analyzing the writing style. Most of the traditional handwriting identification methods tried to extract the handwriting texture features within the whole paragraph. In order to improve the identification accuracy rate, this paper applies non-negative matrix factorization to the off-line handwriting identification of individual characters.Prior to NMF process, two non-negative matrixes need to be initiated at random. In order to increase the stability of the algorithm, this paper uses the initialized values from high dimensional space onto the low dimensional space. Then NMF is applied to the representative characters, which are selected out of a section of characters, to get the handwriting picture’s sub-eigenspaces and eigenvectors for each charactor. Mapping the test samples onto the sub-eigenspaces, get eigenvectors, and calculate the angle relativity and k-nearest neighbor between the eigenvectors and the values of test samples, then classify the handwriting picture. The identification accuracy rate for the complicated Chinese characters that have left-right structure is relatively higher. This results show that NMF outperforms the traditional PCA-base representation. Therefore, non-negative matrix factorization technology has potential in the analysis of writer identification.更多还原