# A Novel Dataset for Non-Destructive Inspection of Handwritten Documents

arXiv: https://arxiv.org/abs/2401.04448 · HTML: https://mlsanity.com/paper/2401.04448/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 9 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Fighting deepfakes by detecting GAN DCT anomalies](https://mlsanity.com/paper/2101.09781/) (2021)
- [FragNet: Writer Identification using Deep Fragment Networks](https://mlsanity.com/paper/2003.07212/) (2020)
- [Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images](https://mlsanity.com/paper/1809.10954/) (2018)
- [Improving Video Deepfake Detection: A DCT-Based Approach with Patch-Level Analysis](https://mlsanity.com/paper/2310.11204/) (2023)
- [Innovative Methods for Non-Destructive Inspection of Handwritten Documents](https://mlsanity.com/paper/2310.11217/) (2023)

## Built on by (most cited first, 1 of 1)

- [Are We on the Right Way for Assessing Document Retrieval-Augmented Generation?](https://mlsanity.com/paper/2508.03644/) (2025)

## Abstract (arXiv)

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/.