# The Research on the Handwriting Stability in Different Devices and Conditions

## TL;DR

This study examines digital handwriting stability using different devices and tools, creating a large dataset and showing that styluses improve accuracy in identifying writers.

## Contribution

A new large-scale digital handwriting dataset and insights into the effectiveness of styluses and alignment techniques for forensic analysis.

## Key findings

- A new handwriting dataset with 16,500 DCSs was created, including Chinese, English, and number characters.
- Styluses offer more accurate distinctions between same- and different-writer samples than finger-based writing.
- Preprocessing with character centroid alignment reduces average accumulated distance, improving handwriting stability analysis.

## Abstract

The purpose of this study is to conduct a rigorous and systematic investigation into the emerging field of digital handwriting identification and to establish a representative, large-scale digital handwriting dataset. In parallel, it aims to develop advanced technologies and tools to enhance sample quality, thereby providing a robust technical foundation for future document examination and promoting the continued evolution and application of forensic science in the digital era.

What are the main findings?
A new handwriting dataset, comprising 16,500 handwriting DCSs, including Chinese characters, English characters, and numbers, has been constructed.The use of styluses provides more precise distinctions between same- and different-writer samples compared with direct finger-based writing.

A new handwriting dataset, comprising 16,500 handwriting DCSs, including Chinese characters, English characters, and numbers, has been constructed.

The use of styluses provides more precise distinctions between same- and different-writer samples compared with direct finger-based writing.

What are the implications of the main findings?
In most cases, FastDTW demonstrated better separation the SC-DTW method used for on-line signature verification under the examined conditions.As with the ENFSI Best Practice Manual for Forensic Handwriting Examination, it is highly recommended to obtain reference handwriting samples in documentation examination using the same writing equipment or under the same writing conditions.

In most cases, FastDTW demonstrated better separation the SC-DTW method used for on-line signature verification under the examined conditions.

As with the ENFSI Best Practice Manual for Forensic Handwriting Examination, it is highly recommended to obtain reference handwriting samples in documentation examination using the same writing equipment or under the same writing conditions.

With the rapid advancement of technology in recent years, signatures on contracts and documents have increasingly shifted from traditional handwritten forms on paper to digital handwritten signatures executed on devices (hereafter referred to as digital tablets). This transition introduces new challenges for forensic document examination due to the differences in writing instruments. According to the European Network of Forensic Science Institutes (ENFSI), a Digital Capture Signature (DCS) refers to data points captured during the writing process on digital devices such as tablets, smartphones, or signature pads. In addition to retaining the visual image of the signature, DCS provides more information previously unavailable, including pen pressure, stroke order, and writing speed. These features possess potential forensic value and warrant further study and evaluation. This study employs three devices—Samsung Galaxy Tab S10, Apple iPad Pro, and Apple iPad Mini—together with their respective styluses as experimental tools. Using custom-developed handwriting capture software for both Android and iOS platforms, we simulated signature-writing scenarios common in the financial and insurance industries. Thirty participants were asked to provide samples of horizontal Chinese, English, and number writings (FUJ-IRB NO: C113187), which were subsequently normalized and segmented into characters. For analysis, we adopted distance-based time-series alignment algorithms (FastDTW and SC-DTW) to match writing data across different instances (intra- and inter-writer). The accumulated distances between corresponding data points, such as coordinates and pressure, were used to assess handwriting stability and to study the differences between same-writer and different-writer samples. The findings indicate that preprocessing through character centroid alignment, followed by the analysis, substantially reduces the average accumulated distance of handwriting. This procedure quantifies the stability of an individual’s handwriting and enables differentiation between same-writer and different-writer scenarios based on the distribution of DCS distances. Furthermore, the use of styluses provides more precise distinctions between same- and different-writer samples compared with direct finger-based writing. In the context of rapid advancements in artificial intelligence and emerging technologies, this preliminary study aims to contribute foundational insights into the forensic application of digital signature examination.