An Open-Source Annotated Dataset of Ghana Currency Images

Published: 19 May 2022| Version 1 | DOI: 10.17632/vws5r8mj4w.1
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Description

The field of deep learning has led to remarkable advancements in many areas, including banking. Identifying currency denomination type and model is challenging due to intraclass variation and different illumination conditions. This dataset presents the Ghana Currency image dataset (GC3558) of 3558 color images in 13 classes created from a high-resolution camera. The class consists of coin and paper notes: 10 pesewas coin, 20 pesewas coin, 50 pesewas coin, 1 cedi coin, 2 cedis coin, 1 cedi note, 2 cedis note, 5 cedis note, 10 cedis note, 20 cedis note, 50 cedis note, 100 cedis note and 200 cedis note. All images are de-identified, validated, and freely available for download to AI researchers. The dataset will help researchers evaluate their machine learning models on real-world data.

Files

Steps to reproduce

The denominations of the Ghana currency were collected using a high-resolution camera device. The original .jpg images of currency were in varied dimensions (1512 x 2016), (1560 x 2080), (2080 x 1560), and (1080 x 1440). These images are resized to 128 x 128 dimensions. There are total 13 classes of the Ghana currency namely 10_pesewas_coin, 20_pesewas_coin, 50_pesewas_coin, 1_cedi_coin, 2_cedi_coin, 1_cedi_note, 2_cedi_note, 5_cedi_note, 10_cedi_note, 20_cedi_note, 50_cedi_note, 100_cedi_note, and 200_cedi_note. The images were captured from various environmental conditions like white background, dark background, yellow background, and illuminated background.

Institutions

University of Energy and Natural Resources

Categories

Machine Learning, Deep Learning

License