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First investigation of feasibility of contact-less non-destructive optical sensors to detect, acquire and digitally process forensic handwriting based on pressure information

机译:首先调查接触不损坏光学传感器的可行性,以基于压力信息检测,获取和数字处理法医手写

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Both in biometric and forensic sciences, handwriting modality plays an important role. While in biometrics, the sub-discipline of online analysis utilises recordings of the actual writing process, offline analysis has a close link to handwriting forensics. In the later domain, goals comprise for example writer identification or uncover indented writing of found writing traces on documents. In this paper we study the general feasibility of a new approach to handwriting forensics by means of three contact-less non-destructive 2D/3D optical sensors providing high resolution images in the nanometer range which have already shown great potential for the digitised crime scene forensics e.g for fingerprints or gun cartridges. We perform a three-step concept: First, in a general sensor review we identify one sensor which appears most appropriate for our initial use cases, along with appropriate parametrization. Secondly, we propose a first test setup for the acquisition of handwriting traces on paper. The corresponding data is captured from the first and second layer of a five sheets paper stack. Thirdly, we present and discuss our first results of digital image pre-processing based on topographic information representing the pen pressure during the writing process. Our pre-processing approach is focused on segmenting the writing trace in topographical data. For segmentation of the trace we apply different linear filter like mean or binomial as well as non-linear filter like median.
机译:两者都在生物识别和法医学中,手写方式起着重要作用。在生物识别学中,在线分析的子学科利用实际写作过程的录音,离线分析与手写取证有关。在后来的域中,目标包括例如作家识别或发现在文档上的发现写迹线的缩小写入。在本文中,我们通过三个接触的非破坏性2D / 3D光学传感器研究了一种新方法对手写取证的一般可行性,其在纳米范围内提供高分辨率图像,这已经为数字化犯罪现场取证的巨大潜力例如,用于指纹或枪盒。我们执行三步概念:首先,在一般的传感器审查中,我们识别一个传感器,这些传感器最适合我们的初始用例,以及适当的参数化。其次,我们提出了第一次测试设置,以获取纸上的手写痕迹。相应的数据从五张纸堆叠的第一和第二层捕获。第三,我们在写入过程中表示笔压的地形信息,我们展示并讨论了我们的第一件数字图像预处理结果。我们的预处理方法专注于在地形数据中分段写入轨迹。对于迹线的分割,我们应用不同的线性滤波器,如平均值或二项式以及类似中值的非线性滤波器。

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