Image based ringgit banknote recognition for visually impaired
Visually impaired people face a number of difficulties in order to interact with the environment because most of the information encoded is visual. Visual impaired people faced a problem in identifying and recognizing the different currency. There are many devices available in the market but not acc...
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Universiti Teknikal Malaysia Melaka
2017
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my.utm.766132018-04-30T13:38:23Z http://eprints.utm.my/id/eprint/76613/ Image based ringgit banknote recognition for visually impaired Jasmin Sufri, N. A. Rahmad, N. A. As'ari, M. A. Zakaria, N. A. Jamaludin, M. N. Ismail, L. H. Mahmood, N. H. TK Electrical engineering. Electronics Nuclear engineering Visually impaired people face a number of difficulties in order to interact with the environment because most of the information encoded is visual. Visual impaired people faced a problem in identifying and recognizing the different currency. There are many devices available in the market but not acceptable to detect Malaysian ringgit banknote and very pricey. Many studies and investigation have been done in introducing automated bank note recognition system and can be separated into vision based system or sensor based system. The objective of this project was to develop an automated system or algorithm that can recognize and classify different Ringgit Banknote for visually impaired person based on banknote image. In this project, the features extraction of the RGB values in six different classes of banknotes (RM1, RM5, RM10, RM20, RM 50, and RM100) was done by using Matlab software. Three features called RB, RG and GB extracted from the RGB values were used for the classification algorithms such as k-Nearest Neighbors (k-NN) and Decision Tree Classifier (DTC) for recognizing each classes of banknote. Ten-fold cross validation was used to select the optimized k-NN and DTC, which was based on the smallest cross validation loss. After that, the performance of optimize k-NN and DTC model was presented in confusion matrix. Result shows that the proposed k-NN and DTC model managed to achieve 99.7% accuracy with the RM50 class causing major reduction in performance. In conclusion, an image based automated system that can recognize the Malaysian banknote using k-NN and DTC classifier has been successfully developed. Universiti Teknikal Malaysia Melaka 2017 Article PeerReviewed Jasmin Sufri, N. A. and Rahmad, N. A. and As'ari, M. A. and Zakaria, N. A. and Jamaludin, M. N. and Ismail, L. H. and Mahmood, N. H. (2017) Image based ringgit banknote recognition for visually impaired. Journal of Telecommunication, Electronic and Computer Engineering, 9 (3-9). pp. 103-111. ISSN 2180-1843 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041929180&partnerID=40&md5=2fbc2a17f7ceab9331802aa56b5d6687 |
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TK Electrical engineering. Electronics Nuclear engineering Jasmin Sufri, N. A. Rahmad, N. A. As'ari, M. A. Zakaria, N. A. Jamaludin, M. N. Ismail, L. H. Mahmood, N. H. Image based ringgit banknote recognition for visually impaired |
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Visually impaired people face a number of difficulties in order to interact with the environment because most of the information encoded is visual. Visual impaired people faced a problem in identifying and recognizing the different currency. There are many devices available in the market but not acceptable to detect Malaysian ringgit banknote and very pricey. Many studies and investigation have been done in introducing automated bank note recognition system and can be separated into vision based system or sensor based system. The objective of this project was to develop an automated system or algorithm that can recognize and classify different Ringgit Banknote for visually impaired person based on banknote image. In this project, the features extraction of the RGB values in six different classes of banknotes (RM1, RM5, RM10, RM20, RM 50, and RM100) was done by using Matlab software. Three features called RB, RG and GB extracted from the RGB values were used for the classification algorithms such as k-Nearest Neighbors (k-NN) and Decision Tree Classifier (DTC) for recognizing each classes of banknote. Ten-fold cross validation was used to select the optimized k-NN and DTC, which was based on the smallest cross validation loss. After that, the performance of optimize k-NN and DTC model was presented in confusion matrix. Result shows that the proposed k-NN and DTC model managed to achieve 99.7% accuracy with the RM50 class causing major reduction in performance. In conclusion, an image based automated system that can recognize the Malaysian banknote using k-NN and DTC classifier has been successfully developed. |
format |
Article |
author |
Jasmin Sufri, N. A. Rahmad, N. A. As'ari, M. A. Zakaria, N. A. Jamaludin, M. N. Ismail, L. H. Mahmood, N. H. |
author_facet |
Jasmin Sufri, N. A. Rahmad, N. A. As'ari, M. A. Zakaria, N. A. Jamaludin, M. N. Ismail, L. H. Mahmood, N. H. |
author_sort |
Jasmin Sufri, N. A. |
title |
Image based ringgit banknote recognition for visually impaired |
title_short |
Image based ringgit banknote recognition for visually impaired |
title_full |
Image based ringgit banknote recognition for visually impaired |
title_fullStr |
Image based ringgit banknote recognition for visually impaired |
title_full_unstemmed |
Image based ringgit banknote recognition for visually impaired |
title_sort |
image based ringgit banknote recognition for visually impaired |
publisher |
Universiti Teknikal Malaysia Melaka |
publishDate |
2017 |
url |
http://eprints.utm.my/id/eprint/76613/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041929180&partnerID=40&md5=2fbc2a17f7ceab9331802aa56b5d6687 |
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1643657360688807936 |
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13.18916 |