Sadyk, U.; Bozshina, A.; Baimukashev, R.; Turan, C. Applying Gray Level Co-occurrence Matrix features and Learning Vector Quantization for Kazakhstan Banknote Classification. Preprints2024, 2024011606. https://doi.org/10.20944/preprints202401.1606.v1
APA Style
Sadyk, U., Bozshina, A., Baimukashev, R., & Turan, C. (2024). Applying Gray Level Co-occurrence Matrix features and Learning Vector Quantization for Kazakhstan Banknote Classification. Preprints. https://doi.org/10.20944/preprints202401.1606.v1
Chicago/Turabian Style
Sadyk, U., Rashid Baimukashev and Cemil Turan. 2024 "Applying Gray Level Co-occurrence Matrix features and Learning Vector Quantization for Kazakhstan Banknote Classification" Preprints. https://doi.org/10.20944/preprints202401.1606.v1
Abstract
When it comes to financial transactions and counterfeit detection systems, banknote classification is crucial. In this paper, we propose an approach for the classification of Kazakhstan banknote images integrating Learning Vector Quantization (LVQ) with Statistical Texture Feature Extraction using the Gray Level Co-occurrence matrix (GLCM). Our methodology demonstrates effectiveness in accurately classifying banknote images, as evidenced by experimental results. The comprehensive testing scenarios show promising outcomes, with the combination of GLCM and Color Histogram under the LVQ algorithm achieving a high accuracy of 94.87 percent at distance 1 and 90°. These findings indicate the robustness and practical viability of the proposed method for authenticating banknotes.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
