Transfer Learning-Based Bhutanese Currency Recognition
Currency recognition is an important field in the area of pattern recognition. Recent improvements in the field of deep learning have increased its ability to recognize complex features from images. The goal of this research was to create a first-ever Bhutanese dataset and train using transfer learn...
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2024-01-01
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| Series: | Computing Open |
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| Online Access: | https://www.worldscientific.com/doi/10.1142/S2972370123500071 |
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| author | Yonten Jamtsho Pema Yangden Sonam Wangmo Nima Dema |
| author_facet | Yonten Jamtsho Pema Yangden Sonam Wangmo Nima Dema |
| author_sort | Yonten Jamtsho |
| collection | DOAJ |
| description | Currency recognition is an important field in the area of pattern recognition. Recent improvements in the field of deep learning have increased its ability to recognize complex features from images. The goal of this research was to create a first-ever Bhutanese dataset and train using transfer learning techniques. This paper proposes three different pre-trained models to train the Bhutanese currency datasets. A parameter fine-tuning was applied to get better accuracy from the custom dataset. The currency images were gathered using crowdsourcing techniques and then the data augmentation was performed to generate 1000 images per class. The datasets were split into the train, test and validation sets with the ratio of 70:20:10. The train and test sets were used while training the model and after the training, it was validated using the validation set. After training the model for certain epochs, VGG16 architecture outperformed other models with a training accuracy of 99.82%, a test accuracy of 99.12% and a validation accuracy of 95.5%. In the future, a greater number of images need to be included in the datasets and trained using other pre-trained models. |
| format | Article |
| id | doaj-art-421adefeccc249d3aa2cb37c77134927 |
| institution | Kabale University |
| issn | 2972-3701 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Computing Open |
| spelling | doaj-art-421adefeccc249d3aa2cb37c771349272025-02-04T03:24:12ZengWorld Scientific PublishingComputing Open2972-37012024-01-010210.1142/S2972370123500071Transfer Learning-Based Bhutanese Currency RecognitionYonten Jamtsho0Pema Yangden1Sonam Wangmo2Nima Dema3Gyalpozhing College of Information Technology, Royal University of Bhutan, Thimphu, BhutanGyalpozhing College of Information Technology, Royal University of Bhutan, Thimphu, BhutanGyalpozhing College of Information Technology, Royal University of Bhutan, Thimphu, BhutanGyalpozhing College of Information Technology, Royal University of Bhutan, Thimphu, BhutanCurrency recognition is an important field in the area of pattern recognition. Recent improvements in the field of deep learning have increased its ability to recognize complex features from images. The goal of this research was to create a first-ever Bhutanese dataset and train using transfer learning techniques. This paper proposes three different pre-trained models to train the Bhutanese currency datasets. A parameter fine-tuning was applied to get better accuracy from the custom dataset. The currency images were gathered using crowdsourcing techniques and then the data augmentation was performed to generate 1000 images per class. The datasets were split into the train, test and validation sets with the ratio of 70:20:10. The train and test sets were used while training the model and after the training, it was validated using the validation set. After training the model for certain epochs, VGG16 architecture outperformed other models with a training accuracy of 99.82%, a test accuracy of 99.12% and a validation accuracy of 95.5%. In the future, a greater number of images need to be included in the datasets and trained using other pre-trained models.https://www.worldscientific.com/doi/10.1142/S2972370123500071Ngultrumtransfer learningfine-tuningdeep learningBhutanese currency |
| spellingShingle | Yonten Jamtsho Pema Yangden Sonam Wangmo Nima Dema Transfer Learning-Based Bhutanese Currency Recognition Computing Open Ngultrum transfer learning fine-tuning deep learning Bhutanese currency |
| title | Transfer Learning-Based Bhutanese Currency Recognition |
| title_full | Transfer Learning-Based Bhutanese Currency Recognition |
| title_fullStr | Transfer Learning-Based Bhutanese Currency Recognition |
| title_full_unstemmed | Transfer Learning-Based Bhutanese Currency Recognition |
| title_short | Transfer Learning-Based Bhutanese Currency Recognition |
| title_sort | transfer learning based bhutanese currency recognition |
| topic | Ngultrum transfer learning fine-tuning deep learning Bhutanese currency |
| url | https://www.worldscientific.com/doi/10.1142/S2972370123500071 |
| work_keys_str_mv | AT yontenjamtsho transferlearningbasedbhutanesecurrencyrecognition AT pemayangden transferlearningbasedbhutanesecurrencyrecognition AT sonamwangmo transferlearningbasedbhutanesecurrencyrecognition AT nimadema transferlearningbasedbhutanesecurrencyrecognition |