Ethiopian Banknote Denomination Classification & Fake Detection System

An optimal feature extraction and classification technique

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Forgery is one of the critical problems affecting cash transactions. Forged banknotes are becoming serious threats hampering the smooth transactions in Ethiopia. Hence, the availability of such fake notes in the market needs the automation of the money transaction system. The banking industries are unable to fully utilize self-serving devices including ATMs intensively. Nevertheless, banks have not yet utilized a reliable recognition system to identify forged banknotes. This calls for the development of a better authenticity verification system. In this study, we have examined the color momentum, SIFT, GLCM, combination of SIFT, color, and GLCM, and convolutional neural network as a feature extraction technique and support vector machine, K- nearest neighbor classifier, and feed-forward artificial neural network as a classifier to design Ethiopian banknote recognition system. In order to minimize the effect of noisy data, we have employed an intensive image preprocessing tasks, like image histogram equalization and adaptive median filter-based image denoising.


Asfaw Alene


Mr. Asfaw Alene is keen to begin a career in Computer science. He has 11 years of working experience in different ICT positions with a different institution –Teaching in high school and university, ICT experts, system software administrator, network administrator, senior network administrator, data center administrator, and lecturer positions. 

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LAP LAMBERT Academic Publishing


Ethiopian banknote; Convolution Neural Network; Feed Forward Artificial Neural Network; GLCM, SIFT, Color momentum, Support Vector Machine, K-Nearest Neighbor, CNN, FFANN, Ethiopian banknote classification, Ethiopian banknote authentication system

Product category:

TECHNOLOGY / Electronics / General