Abstract Banknote recognition means classifying the currency (coin and paper) to the correct class. In this paper, we developed a dataset for Jordanian currency. After that we applied automatic mobile recognition system using a smartphone on the dataset using scale-invariant feature transform (SIFT) algorithm. This is the first attempt, to the best of the authors knowledge, to recognize both coins and paper banknotes on a smartphone using SIFT algorithm. SIFT has been developed to be the most robust and efficient local invariant feature descriptor. Color provides significant information and important values in the object description process and matching tasks. Many objects cannot be classified correctly without their color features. We compared between two approaches colored local invariant feature descriptor (color SIFT approach) and gray image local invariant feature descriptor (gray SIFT approach). The evaluation results show that the color SIFT approach outperforms the gray SIFT approach in terms of processing time and accuracy.
Doush and AL-Btoush REF develop an automatic banknote recognition, to classify Jordanian currency to the correct class.
Currency recognition using a smartphone: Comparison between color SIFT and gray scale SIFT algorithms
Journal of King Saud University - Computer and Information Sciences
Computer Science