• Nur Anis Jasmin Sufri School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 UTM Johor Bahru, Johor, Malaysia
  • Lina Suhaili Rosidi School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 UTM Johor Bahru, Johor, Malaysia
  • Muhammad Amir As’ari School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 UTM Johor Bahru, Johor, Malaysia



Visual impaired, Deep learning, AlexNet, GoogleNet, MobileNetV2, Coins recognition


Currency recognition has been widely developed using various types of techniques and able to assist people who have a visual impairment. Machine learning is one of the methods implemented where deep learning architecture is one of them. The deep learning approach is reliable and can be used in detection and recognition of objects based on images. As currency recognition has been developed for other currencies, thus in this project, currency recognition using Malaysian coins has been developed by modeling Convolutional Neural Network (CNN) in recognizing coin images. Malaysian coins dataset was developed consist of 2400 images of four classes of coins, 5 sen, 10 sen, 20 sen, and 50 sen. In this study, pretrained CNN which are AlexNet, GoogleNet, and MobileNetV2 were formulated in recognizing such coins. Performance of each trained model was evaluated using confusion matrix and GoogleNet obtained the best performance with 99.2% testing accuracy, 99.2% precision, 99.18% recall, and 99.19% F1 score. From the trained model, it can be further developed and implemented in assisting visually impaired persons by producing a prototype using Raspberry Pi and FPGA before it can be clinically tested on the subject.


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How to Cite

Sufri, N. A. J., Rosidi, L. S. ., & As’ari, M. A. (2022). DEEP LEARNING BASED MALAYSIAN COINS RECOGNITION FOR VISUAL IMPAIRED PERSON. ASEAN Engineering Journal, 12(2), 119-126.