Automatic Recognition of Ethiopian Paper Currency
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Date
2014-10
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Addis Ababa University
Abstract
Currency recognition is an image processing technology that is used to identify currency of various countries. Due to the use of currency in day to day life, the importance for automatic methods for currency recognition has been increasing. An efficient currency recognition system is vital for automation in many sectors such as vending machine, railway ticket counter, banking system, shopping mall, currency exchange service, etc. Due to this, automatic currency recognition has been the interest of many researchers and currency recognition was done for different countries’ currencies such as United States (US) dollar, Euro, Chinese Renminbi (RMN), Indian rupee and Mexican peso. However, to the best knowledge of the researcher, there is no any research done towards designing and implementing recognition of Ethiopian currency. The absence of such currency recognition is a big gap in Ethiopia.
This thesis describes the design of automatic recognition of Ethiopian currency. In this research, a software solution which takes the image of an Ethiopian currency from a scanner and camera as an input is proposed. The researcher combined the approaches of currency characteristic comparison and local feature descriptors to design a four level classifier. The design has a categorization component, which is responsible to denominate the currency notes into their respective denomination and verification component which is responsible to validate whether the currency is genuine or not.
Both components of the design are implemented using MATLAB. The design is tested using genuine Ethiopian currencies at different condition, counterfeit Ethiopian currencies and other countries’ currencies. The denomination accuracy for genuine Ethiopian currency, counterfeit currencies and other countries’ currencies is found to be 90.42%, 83.3% and 100% respectively. The verification accuracy is 96.13%. The overall processing time of the model is 1. 986 second. Therefore our model has a good performance with a denomination and verification accuracy more than 90%.
KEY WORDS: Image Processing, Currency Recognition, Speeded Up Robust Feature (SURF) Feature, MATLAB, Hue Saturation Value (HSV), Counterfeit Detection, Classifier, Feature Extraction.
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Keywords
Image Processing; Currency Recognition; Speeded Up Robust Feature (Surf) Feature; Matlab; Hue Saturation Value (Hsv); Counterfeit Detection; Classifier; Feature Extraction.