Writer Identification Using Siamese Networks and Character-Level Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0199901" target="_blank" >RIV/00216305:26220/26:0199901 - isvavai.cz</a>
Alternative codes found
RIV/00007064:K01__/25:N0000038
Result on the web
<a href="http://dx.doi.org/10.1109/ICUMT67815.2025.11268620" target="_blank" >http://dx.doi.org/10.1109/ICUMT67815.2025.11268620</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICUMT67815.2025.11268620" target="_blank" >10.1109/ICUMT67815.2025.11268620</a>
Alternative languages
Result language
angličtina
Original language name
Writer Identification Using Siamese Networks and Character-Level Analysis
Original language description
Writer identification of handwritten text is a task in forensic document analysis, traditionally relying on visual comparison by experts. However, this process is time-consuming and subjective. Although the amount of crime involving handwriting has remained constant, the overall volume of handwritten material has decreased. This paper presents an approach based on a Siamese Neural Network (SNN) to writer identification by analyzing a limited information source - just a single character – A – from samples of Czech handwriting. The main contributions are: (i) the design of an SNN with a five-block convolutional branch combined with voting strategies incorporating an uncertainty zone; and (ii) a detailed experimental comparison of over 400 architecture and hyperparameter configurations in terms of accuracy, F1-score, and decision efficiency. The best model achieved relatively high accuracy – 96.1 % accuracy and a F1-score of 0.932 while abstaining from classification in approximately 57% of ambiguous cases. The trade-off between classification confidence and coverage, the limitations of single-character analysis, and the potential for generalization to open-set scenarios and multimodal inputs are discussed. The proposed approach offers an objective and reproducible method suitable for forensic handwriting analysis.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
<a href="/en/project/VJ02010019" target="_blank" >VJ02010019: Tools for Handwriting fORensics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 17th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
ISBN
979-8-3315-7675-2
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
182-186
Publisher name
IEEE
Place of publication
Italy
Event location
Florence, Italy
Event date
Nov 3, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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