Principal features for Indian currency recognition

Author

Vishnu, R. ; Omman, B.

Author_Institution

Dept. of Comput. Sci. & Eng., SCMS Sch. of Eng. & Technol., Ernakulam, India

fYear

2014

fDate

11-13 Dec. 2014

Firstpage

1

Lastpage

8

Abstract

Currency recognition system is one of the fast growing research fields under image processing. This paper proposes a novel method for Indian currency recognition. Our proposed approach identifies denomination by extracting features like Center Numeral, Shape, RBI Seal, Latent Image and Micro Letter. Principal Component Analysis is used to reduce the dimensions and a similarity based classifier is constructed to predict test sample. Results are also validated by constructing models using classifier implemented using WEKA and testing with unseen samples not considered in feature extraction. Our study demonstrated that center numeral results in an accuracy of 100% with all family of currencies.

Keywords

feature extraction; object recognition; principal component analysis; Indian currency recognition system; RBI seal; WEKA; center numeral; feature extraction; image processing; principal component analysis; similarity based classifier; Accuracy; Covariance matrices; Feature extraction; Principal component analysis; Seals; Shape; Vectors; Classifier; Currency; Pattern Recognition; Principal Component Analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

India Conference (INDICON), 2014 Annual IEEE

Conference_Location

Pune

Print_ISBN

978-1-4799-5362-2

Type

conf

DOI

10.1109/INDICON.2014.7030679

Filename

7030679