Vision Based System for Banknote Recognition Using Different Machine Learning and Deep Learning Approach

Visually impaired people faced a problem in identifying and recognizing the different types of banknote due to some reasons. This problem draws researchers’ attention to introduce an automated banknote recognition system that can be divided into a vision-based system and sensor-based system. The main aim of this study is to have deeper analysis on the effect of region and orientation on the performance of Machine Learning and Deep Learning respectively using Malaysian Ringgit banknotes (RM 1, RM 5, RM 10, RM 20, RM 50 and RM 100). In this project, two experiments conducted on two types of banknote image: different region and orientation captured by using handphone camera in a controlled environment. Feature extraction of the RGB values called RB, RG, and GB from banknote image with different region were used to the machine learning classification algorithms such as k-Nearest Neighbors (kNN) and Decision Tree Classifier (DTC), Support Vector Machine (SVM) and Bayesian Classifier (BC) for recognizing each class of banknote. Banknote image with different orientation was directly feed to AlexNet, a pre-trained model of Convolutional Neural Network (CNN), the most popular image processing structure of Deep Learning Neural Network. Ten-fold cross-validation was used to select the optimized kNN, DTC, SVM, and BC which was based on the smallest cross-validation loss. After that, the performance of kNN, DTC, SVM, BC and AlexNet model was presented in a confusion matrix. Both kNN and DTC achieved 99.7% accuracy but both SVM and BC perform better by succeeded to achieve 100% accuracy. It also can be concluded that AlexNet can only perform great in testing new data if only the data had previously been trained with similar orientation. Orientation does give effect to the performance of AlexNet model.