Paper Title
Utilizing Deep learning Techniques For Detecting Counterfeit Bank Currency
Article Identifiers
Authors
Keywords
: CNN,SVM,Counterfeit currency, Technological Advancements,Image processing,Gray scale conversion,Edge detection,Segmentation,Deep learning
Abstract
Counterfeit Currency has always been an issue which has created a lot of problems in the market. The increasing technological advancements have made the possibility for creating more counterfeit currency which are circulated in the market which reduces the overall economy of the country. There are machines present at banks and other commercial areas to check the authenticity of the currencies. But a common man does not have access to such systems and hence a need for a software to detect fake currency arises, which can be used by common people. This proposed system uses Image Processing to detect whether the currency is genuine or counterfeit. The system is designed completely using Python programming language. It consists of the steps such as gray scale conversion, edge detection, segmentation, etc. which are performed using suitable methods. The first order and second order statistical features are extracted initially from the input and undergoes deeplearning algorithm CNN. The effective feature vectors are given to the SVM classifier unit for classification. The proposed method produced classification accuracy of 95.8 percentage. The experimental results are compared with state of-the methods and produced reliable results.
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How To Cite (APA)
Gudepu Jogesh Babu (May-2024). Utilizing Deep learning Techniques For Detecting Counterfeit Bank Currency. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), 297-339. https://ijnrd.org/papers/IJNRDTH00143.pdf
Issue
Volume 9 Issue 5, May-2024
Pages : 297-339
Other Publication Details
Paper Reg. ID: IJNRD_220045
Published Paper Id: IJNRDTH00143
Downloads: 000122038
Research Area: Computer EngineeringÂ
Author Type: Indian Author
Country: visakhapatnam, Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRDTH00143.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRDTH00143
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