A Unique Feature Based Classifier for Bangla Currency using Deep Learning

A carefully prepared custom dataset containing a wide range of situations and denominations of 10- to 1000 Taka notes ensures that the approach is applicable and useful in the real world. The particular combination of YOLOv8 with ResNet-50 achieves outstanding results, with an amazing 99.82% accuracy in currency detection as well as classification. Extensive studies highlight the system's reliability in a wide range of external circumstances, demonstrating its potential for revolutionary currency identification in areas such as economic activity, financial transactions, and more. This particular study marks a new maturity in currency recognition. This is an important shift towards improved accuracy, sustainability, and general effectiveness in Bangladeshi notes of currency detection and classification in modern life.