Malaysian Coins Recognition Using Machine Learning Methods

No Thumbnail Available
Zainuddin N.N.
Azhari M.S.N.B.N.
Hashim W.
Alkahtani A.A.
Mustafa A.S.
Alkawsi G.
Noman F.
Journal Title
Journal ISSN
Volume Title
Institute of Electrical and Electronics Engineers Inc.
Research Projects
Organizational Units
Journal Issue
Coins have become essential to our daily lives. As a legal tender, identifying, validating, classifying, and sorting coins is a complex process. In essence, automated coin detectors should identify deteriorated or older coins and distinguish between genuine and fake coins. Nonetheless, we identified limited literature examining different Machine Learning (ML) approaches for detecting Malaysian coins. This study investigates machine learning approaches and identifies the most efficient and accurate for Malaysian coin recognition. The model was trained on 311 images of coins and classified into four object categories: 5, 10, 20, and 50 cents. Six classifiers are used to test the training model. For the Grey-Level Co-occurrence Matrix (GLCM) feature extraction, AdaBoost classifiers were the most accurate, whereas K-Nearest Neighbors (KNN) classifiers were the least accurate. Moreover, the Artificial Neural Networks (ANN) classifier had the highest accuracy in the Histogram of Oriented Gradients (HOG) feature, while the Linear Discriminant Analysis (LDA) classifier had the lowest. The study findings and future directions are discussed. � 2021 IEEE.
Adaptive boosting; Discriminant analysis; Machine learning; Nearest neighbor search; Neural networks; Classifieds; Coin recognition; Complex Processes; Daily lives; Images processing; Machine learning approaches; Machine learning methods; Malaysians; Object categories; Training model; Image processing