GC3558: An open-source annotated dataset of Ghana currency images for classification modeling
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. Although, in this domain, many datasets regarding currency denomination t...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340922008228 |
| _version_ | 1797985235206406144 |
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| author | Kwabena Adu Patrick Kwabena Mensah Mighty Abra Ayidzoe Obed Appiah Ebenezer Quayson Christopher Bombie Ninfaakang Michael Opoku |
| author_facet | Kwabena Adu Patrick Kwabena Mensah Mighty Abra Ayidzoe Obed Appiah Ebenezer Quayson Christopher Bombie Ninfaakang Michael Opoku |
| author_sort | Kwabena Adu |
| collection | DOAJ |
| 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. Although, in this domain, many datasets regarding currency denomination type and model, e.g., Indian Currency, Thai Currency, Chinese Currency, U.K. currency, etc., have already been experimented with by different researchers. More datasets are needed from a variety of currencies, especially Ghana currency (cedi). This article presents the Ghana Currency image dataset (GC3558) of 3558 color images in 13 classes created from a high-resolution camera. The dataset is comprised of only genuine currency. 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 A.I. researchers. The dataset will help researchers evaluate their machine learning models on real-world data. |
| first_indexed | 2024-04-11T07:14:06Z |
| format | Article |
| id | doaj.art-09bf79b6ec944a49a3def37ff2ea7bdd |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-04-11T07:14:06Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-09bf79b6ec944a49a3def37ff2ea7bdd2022-12-22T04:38:02ZengElsevierData in Brief2352-34092022-12-0145108616GC3558: An open-source annotated dataset of Ghana currency images for classification modelingKwabena Adu0Patrick Kwabena Mensah1Mighty Abra Ayidzoe2Obed Appiah3Ebenezer Quayson4Christopher Bombie Ninfaakang5Michael Opoku6Corresponding author.; Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, GhanaDepartment of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, GhanaDepartment of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, GhanaDepartment of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, GhanaDepartment of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, GhanaDepartment of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, GhanaDepartment of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, GhanaThe 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. Although, in this domain, many datasets regarding currency denomination type and model, e.g., Indian Currency, Thai Currency, Chinese Currency, U.K. currency, etc., have already been experimented with by different researchers. More datasets are needed from a variety of currencies, especially Ghana currency (cedi). This article presents the Ghana Currency image dataset (GC3558) of 3558 color images in 13 classes created from a high-resolution camera. The dataset is comprised of only genuine currency. 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 A.I. researchers. The dataset will help researchers evaluate their machine learning models on real-world data.http://www.sciencedirect.com/science/article/pii/S2352340922008228DatasetDeep learningClassificationCurrency detectionBanknote recognition |
| spellingShingle | Kwabena Adu Patrick Kwabena Mensah Mighty Abra Ayidzoe Obed Appiah Ebenezer Quayson Christopher Bombie Ninfaakang Michael Opoku GC3558: An open-source annotated dataset of Ghana currency images for classification modeling Data in Brief Dataset Deep learning Classification Currency detection Banknote recognition |
| title | GC3558: An open-source annotated dataset of Ghana currency images for classification modeling |
| title_full | GC3558: An open-source annotated dataset of Ghana currency images for classification modeling |
| title_fullStr | GC3558: An open-source annotated dataset of Ghana currency images for classification modeling |
| title_full_unstemmed | GC3558: An open-source annotated dataset of Ghana currency images for classification modeling |
| title_short | GC3558: An open-source annotated dataset of Ghana currency images for classification modeling |
| title_sort | gc3558 an open source annotated dataset of ghana currency images for classification modeling |
| topic | Dataset Deep learning Classification Currency detection Banknote recognition |
| url | http://www.sciencedirect.com/science/article/pii/S2352340922008228 |
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