ArticleData in brief2025
BanglaTaka: A dataset for classification of Bangladeshi banknotes.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Kazakh banknote image dataset.Data in brief · 2026Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Automated classification of banknotes is essential for financial security and e-transaction systems. Conventional manual authentication is time-consuming and error-prone, which necessitates automated systems. This article introduces BanglaNotes, a benchmark dataset for Bangladeshi banknote denomination classification. The dataset contains 5073 images of Bangladeshi paper currency of nine denominations (2, 5, 10, 20, 50, 100, 200, 500, and 1000 BDT), with high quality and consistent representation. Every image in the dataset is labeled according to its denomination. The dataset is suitable for training and testing machine learning algorithms for currency classification and it supports research in financial automation and deep learning-based classification algorithms. It also offers a benchmark for developing robust algorithms for banknotes' real-life applications in banks, mobile payment systems, and ATMs. The dataset is publicly released to promote innovation and further research in banknote classification and recognition.
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