Srihari Sargur N, Cha Sung-Hyuk, Arora Hina, Lee Sangjik
Departmentof Computer Science and engineering, Center of Excellence for Document Analysis and Recognition, University at Buffalo, State University of New York, 14228-2567, USA.
J Forensic Sci. 2002 Jul;47(4):856-72.
Motivated by several rulings in United States courts concerning expert testimony in general, and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individual. Handwriting samples of 1,500 individuals, representative of the U.S. population with respect to gender, age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by forensic document examiners (FDEs), were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting and very few characters in the writing, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court. The mathematical approach and the resulting software also have the promise of aiding the FDE.
受美国法院关于专家证词（尤其是笔迹证词）的多项裁决的推动，我们开展了一项研究，以客观验证笔迹具有个体性这一假设。我们获取了1500名个体的笔迹样本，这些样本在性别、年龄、种族等方面代表了美国人口。通过使用计算机算法从笔迹扫描图像中提取特征来分析笔迹差异。获得了笔迹的特征属性，例如行间距、倾斜度、字符形状等。这些属性是法医文件检验员（FDE）使用的属性的一个子集，通过机器学习方法用于定量确定个体性。利用笔迹的全局属性和书写中很少的字符，建立了高度自信地确定书写者的能力。这项工作朝着为法庭采信笔迹证据提供科学支持迈出了一步。数学方法和由此产生的软件也有望帮助法医文件检验员。
