Abstract
Forensic handwriting examination is a branch of Forensic Science that aims to examine handwritten documents in order to properly define or hypothesize the manuscript's author. These analysis involves comparing two or more (digitized) documents through a comprehensive comparison of intrinsic local and global features. If a correlation exists and specific best practices are satisfied, then it will be possible to affirm that the documents under analysis were written by the same individual. The need to create sophisticated tools capable of extracting and comparing significant features has led to the development of cutting-edge software with almost entirely automated processes, improving the forensic examination of handwriting and achieving increasingly objective evaluations. This is made possible by algorithmic solutions based on purely mathematical concepts. Machine Learning and Deep Learning models trained with specific datasets could turn out to be the key elements to best solve the task at hand. In this paper, we proposed a new and challenging dataset consisting of two subsets: the first consists of 21 documents written either by the classic ``pen and paper" approach (and later digitized) and directly acquired on common devices such as tablets; the second consists of 362 handwritten manuscripts by 124 different people, acquired following a specific pipeline. Our study pioneered a comparison between traditionally handwritten documents and those produced with digital tools (e.g., tablets). Preliminary results on the proposed datasets show that 90% classification accuracy can be achieved on the first subset (documents written on both paper and pen and later digitized and on tablets) and 96% on the second portion of the data. The datasets are available at this https URL.
Abstract (translated)
司法笔迹分析是一门法医学分支，旨在通过全面比较文书的内在局部和全局特征来正确确定或推测稿件的作者。这些分析涉及将两个或更多（数字化）文件通过比较其内在局部和全局特征的全面对比来完成。如果存在相关性并且满足特定的最佳实践，那么就可以证实分析中的文件是由同一个人所写。为了创建能够提取和比较重要特征的复杂工具，以提高笔迹研究和实现越来越客观的评估，进而发展了具有几乎完全自动化的过程的尖端软件。机器学习和深度学习模型通过针对特定数据集进行训练，可能会成为解决这一任务的的关键要素。在本文中，我们提出了一个新而具有挑战性的数据集，由两个子集组成：第一个子集包括21篇由经典“笔和纸”方法（后来数字化）直接获得的文件，和使用普通设备（如平板电脑）获取；第二个子集包括124个不同人员创作的362篇手写稿件。我们的研究首创了传统手写文件与使用数字工具（如平板电脑）制作的文件之间的比较。初步结果表明，第一个子集中的分类准确率为90%，第二个部分的数据中的分类准确率为96%。数据集可在此链接下载：https://www.academia.edu/39411041/Forensic_Handwriting_Analysis_Dataset
URL
https://arxiv.org/abs/2401.04448
