【作者】 李晓通；

【导师】 郑丽颖；

【作者基本信息】 哈尔滨工程大学 ， 计算机应用技术， 2009， 硕士

【摘要】 随着科学技术的发展,基于笔迹的身份鉴别方法已经成为重要生物特征识别手段之一,广泛应用于公安、司法、考古、金融、电子商务等领域。近年来,社会对计算机笔迹鉴别技术提出了新的要求。笔迹特征提取技术作为笔迹鉴别过程中的关键技术,直接影响鉴别效果。研究特征提取技术具有十分重要的理论意义和实用价值。本文在对已有的笔迹特征提取方法进行详细研究的基础上,提出了将文本分块,并对各文本块提取重心特征、形状特征,最后结合文本整体的纹理特征进行笔迹鉴别的方法。其中重心特征提取的是各个笔迹块的重心,这些重心整体上反映了书写的笔迹相对于水平方向上的偏移,是一种与文本无关的笔迹特征。形状特征是在分析了几何矩物理性质的前提下提出的,通过将笔迹分块后提取笔迹块的形状信息作为特征。也是一种文本无关的笔迹特征,纹理特征反映了笔迹的纹理特性。它是通过对笔迹图像进行Gabor滤波获得的。这种特征完全摆脱了文本内容的限制,是一种被广泛使用的特征。为了判别所提取笔迹特征的有效性,论文设计了一个简单的笔迹鉴别系统。这个系统包括预处理模块、重心特征提取与分类模块、形状特征提取与分类模块、纹理特征提取与分类模块,对各个识别结果进行综合的模块。通过这几个模块对所提出的特征提取方法进行了测试,结果表明:基于三种特征的鉴别方法都达到了良好的效果,而且三种方法的综合效果最佳。更多还原

【Abstract】 With the development of science, writer identification (WI) based on handwriting has become an important technology of biometric personal identification. This technology has been widely used in the public security, administration of justice, archaeology, finance and electronic business areas. In recent years, social backgrounds urge more achievements in computer (WI). Features extraction is an important part of handwriting recognition, and has a direct impact on the recognition results. So researching on feature extraction technology has important theoretical significance and application value.After detailed researching on the related technology of handwriting feature extraction, this thesis proposes to segment handwriting image to sub-image and extract the center of gravity features, shape features of each sub-image, then extract texture features for the whole image to realize handwriting identification, and at last to synthesize the results of each way to get the final result. In these ways the central of gravity features reflect handwriting gravity, it is the performance of writing habits and it is also a kind of text independent features. Shape features are proposed after analyzing the physical properties of geometric moments. These features are extracted from handwriting blocks, and it is also a text independent feature. Texture features reflect the texture characteristics of handwriting. These features are obtained by filtering image used Gabor filter. It is a kind of text independent features absolutely and it is used widely.Finally in this thesis, I design a simple handwriting identification system for testing effectiveness of each feature The system includes pre-processing module, the central of gravity feature extraction and classification modules, shape features extraction and classification modules, texture features extraction and classification modules, also includes an integrated module for each recognition result. Each proposed method has been tested through these modules. Results showed that: the handwriting identification based on these three kinds’ features achieves good results, and integrated result of each way can get better results.更多还原