一种用于手写文档非破坏性检测的新型数据集
A Novel Dataset for Non-Destructive Inspection of Handwritten Documents
AI总结：
本文提出一个用于法医笔迹分析的新型数据集，包含传统纸笔与平板电脑书写的文档，并首次对两类书写方式进行比较，初步分类准确率分别达90%和96%。
摘要：
法医笔迹检验是法医学的一个分支，旨在检验手写文档，以便正确定义或假设手稿的作者。这些分析涉及通过全面比较内在的局部和全局特征来比对两个或更多（数字化）文档。如果存在相关性且满足特定的最佳实践，那么就可以断言所分析的文档是由同一个人书写的。创建能够提取和比较显著特征的复杂工具的需求，推动了几乎完全自动化流程的尖端软件的发展，从而改进了笔迹的法医检验，并实现了越来越客观的评估。这得益于基于纯数学概念的算法解决方案。使用特定数据集训练的机器学习和深度学习模型可能成为最佳解决当前任务的关键要素。在本文中，我们提出了一个新的、具有挑战性的数据集，该数据集由两个子集组成：第一个子集包含21份文档，这些文档要么通过经典的“纸笔”方式书写（随后被数字化），要么直接在平板电脑等常见设备上获取；第二个子集包含由124个不同的人书写的362份手写手稿，按照特定的流程获取。我们的研究开创性地对传统手写文档与使用数字工具（例如平板电脑）生成的文档进行了比较。在所提出的数据集上的初步结果表明，在第一个子集（既写在纸笔上随后数字化又写在平板电脑上的文档）上可以达到90%的分类准确率，在第二部分数据上可以达到96%。数据集可在 https://iplab.dmi.unict.it/mfs/forensic-handwriting-analysis/novel-dataset-2023/ 获取。
英文摘要：
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 https://iplab.dmi.unict.it/mfs/forensic-handwriting-analysis/novel-dataset-2023/.
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