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Data · dataset · 2025

A Unique Feature Based Classifier for Bangla Currency using Deep Learning

Listed in Teesside University Research Data Repository

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.

Description

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.

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Teesside University Research Data Repositoryoai:data.mendeley.com/2r8367vkpm.18 d agoJSON v1
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