| Issue |
E3S Web of Conf.
Volume 508, 2024
International Conference on Green Energy: Intelligent Transport Systems - Clean Energy Transitions (GreenEnergy 2023)
|
|
|---|---|---|
| Article Number | 03011 | |
| Number of page(s) | 7 | |
| Section | IoT, AI and Data Analytics | |
| DOI | https://doi.org/10.1051/e3sconf/202450803011 | |
| Published online | 05 April 2024 | |
Off-line handwritten signature verification based on machine learning
Ferghana branch of Tashkent university of information technologies named after Muhammad al-Kwarizmi., Ferghana, Uzbekistan
* Corresponding author: mavlonbek5488@gmail.com
This paper describes the results of recognizing handwritten signatures. For the experiments, the database of handwritten signatures BHSig260-Bengali, BHSig260-Hindi, CEDAR and TUIT was used. For classification, four options were used to reduce the signatures to sizes: 200×120, 250×150, 300×150 and 400×200 pixels. These images served as input for the proposed network architecture. As a result of testing the proposed approach, the average accuracy of correct classification of signatures on images of size 250×150 was achieved: for the CEDAR database it was 94.38%, for the BHSig260-Hindi database it was 95.63%, for the BHSig260-Bengali database it was 97.50% and for TUIT base is 90.04%.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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