Money Recognition for the Visually Impaired: A Case Study on Sri Lankan Banknotes
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913700317757440 |
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| author | Bandara, Akshaan |
| author_facet | Bandara, Akshaan |
| contents | Currency note recognition is a critical accessibility need for blind individuals, as identifying banknotes accurately can impact their independence and security in financial transactions. Several traditional and technological initiatives have been taken to date. Nevertheless, these approaches are less user-friendly and have made it more challenging for blind people to identify banknotes. This research proposes a user-friendly stand-alone system for the identification of Sri Lankan currency notes. A custom-created dataset of images of Sri Lankan currency notes was used to fine-tune an EfficientDet model. The currency note recognition model achieved 0.9847 AP on the validation dataset and performs exceptionally well in real-world scenarios. The high accuracy and the intuitive interface have enabled blind individuals to quickly and accurately identify currency denominations, ultimately encouraging accessibility and independence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14267 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Money Recognition for the Visually Impaired: A Case Study on Sri Lankan Banknotes Bandara, Akshaan Computer Vision and Pattern Recognition Currency note recognition is a critical accessibility need for blind individuals, as identifying banknotes accurately can impact their independence and security in financial transactions. Several traditional and technological initiatives have been taken to date. Nevertheless, these approaches are less user-friendly and have made it more challenging for blind people to identify banknotes. This research proposes a user-friendly stand-alone system for the identification of Sri Lankan currency notes. A custom-created dataset of images of Sri Lankan currency notes was used to fine-tune an EfficientDet model. The currency note recognition model achieved 0.9847 AP on the validation dataset and performs exceptionally well in real-world scenarios. The high accuracy and the intuitive interface have enabled blind individuals to quickly and accurately identify currency denominations, ultimately encouraging accessibility and independence. |
| title | Money Recognition for the Visually Impaired: A Case Study on Sri Lankan Banknotes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.14267 |