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Updated: Mar 12, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Kazakh banknote image dataset.

Ualikhan Sadyk¹, Makhambet Yerzhan², Cemil Turan¹

¹Department of Computer Science, SDU University, Abylaikhan St. 1/1, Kaskelen, Kazakhstan.

|March 11, 2026

Summary

This summary is machine-generated.

This study introduces a new image dataset of Kazakhstani tenge banknotes for computer vision research. The dataset aids in developing and testing banknote recognition systems under diverse conditions.

Keywords:

Banknote recognition Central Asian currency Currency detection Machine Learning

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Area of Science:

Computer Vision
Pattern Recognition
Machine Learning

Background:

Developing robust automated systems for currency recognition is crucial.
Existing datasets may lack diversity in real-world conditions like varied backgrounds and lighting.
Kazakhstani tenge recognition requires specialized datasets for accurate classification and detection.

Purpose of the Study:

To present a comprehensive image dataset of Kazakhstani banknotes.
To facilitate research in computer vision and pattern recognition tasks.
To support the development of advanced banknote identification algorithms.

Main Methods:

Collected and curated a dataset of Kazakhstani tenge banknotes.
Photographed banknotes across seven denominations (500-20,000 Tenge) and a mixed category.
Ensured image diversity with varied backgrounds, lighting, and multiple/single note configurations.

Main Results:

Dataset includes over 100 images per denomination and 1000+ for mixed categories.
Specific subsets address old/new designs and single/multiple note scenarios for the 5000 Tenge.
Images capture realistic variations in background and illumination.

Conclusions:

The dataset provides a valuable resource for training and evaluating banknote recognition models.
Enables research on model robustness against environmental variations.
Supports transfer learning, bias studies, and comparative evaluations in currency recognition.