A Novel Dataset for Non-Destructive Inspection of Handwritten Documents
Eleonora Breci Luca Guarnera Sebastiano Battiato
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/.