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Journals / Acta Infologica / 2024 / Volume: 8 Issue: 2

Turkish Lira Banknote Classification using Transfer Learning and Deep Learning

Mirsat Yeşiltepe(Yıldız Technical University, Department of Mathematical Engineering)0000-0003-4433-5606
Harun Elkiran(İstanbul Sabahattin Zaim University, Department of Computer Engineering)0000-0002-5834-6210
Jawad Rasheed(İstanbul Sabahattin Zaim University, Department of Computer Engineering)0000-0003-3761-1641
Pages
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Publication Date
2024-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In addition, models such as DenseNet169 and DenseNet121 also demonstrated high performance; however, MobileNetV2 struggled with smaller images.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:With the increasing exchange of foreign currencies due to globalization, there is a need for systems that can recognize and validate multiple currencies in real time.
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Key Findings & Data:The dataset comprises 6901 images of six different denominations (5 TL, 10 TL, 20 TL, 50 TL, 100 TL, and 200 TL) under various conditions, such as flat, angled, curved, and bent.
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Conclusion & Impact:In addition, models such as DenseNet169 and DenseNet121 also demonstrated high performance; however, MobileNetV2 struggled with smaller images.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:DenseNet201 · Optimizer · Banknote

Abstract

With the increasing exchange of foreign currencies due to globalization, there is a need for systems that can recognize and validate multiple currencies in real time. Such systems facilitate smooth international transactions and support the finance sector in dealing with diverse currencies. This study focuses on classifying Turkish banknotes using deep learning models. The dataset comprises 6901 images of six different denominations (5 TL, 10 TL, 20 TL, 50 TL, 100 TL, and 200 TL) under various conditions, such as flat, angled, curved, and bent. The proposed model imple ments pre-trained models, including VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, MobileNet, and MobileNetV2, to classify the images. Different image sizes (50x50, 100x100, 150x150, and 200x200) and optimizers (SGD, RMSprop, Adam, Adamax, etc.) were tested to determine the most effective combinations. The best result was achieved with DenseNet201 with an image size of 200 and the SGDoptimizer, achieving an accuracy of 98.84% in 12 epochs. Smaller image sizes (50x50) resulted in reduced performance for all models. In addition, models such as DenseNet169 and DenseNet121 also demonstrated high performance; however, MobileNetV2 struggled with smaller images.

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Cite This Article

Yeşiltepe, M., Elkiran, H., Rasheed, J. (2024). Turkish Lira Banknote Classification using Transfer Learning and Deep Learning. Acta Infologica, Volume: 8 Issue: 2. https://doi.org/10.26650/acin.1447456

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