Automatic detection and recognition of Indian currency note has gained a lot of research attention in recent years particularly due to its vast potential applications. In this paper we introduce a new recognition method for Indian currency using computer vision. It is shown that Indian currencies can be classified based on a set of unique non discriminating features such as color, dimension and most importantly the Identification Mark (unique for each denomination) mentioned in RBI guidelines. Firstly the dominant color and the aspect ratio of the note are extracted. After this the segmentation of the portion of the note containing the unique I.D. Mark is done. From these segmented image, feature extraction is done using Fourier Descriptors. As each note has a unique shape as the I.D. Mark, the classification of these shapes is done with the help of Artificial Neural Network. After feature extraction, the denominations are recognized based on the developed algorithm. The success rate of the proposed system is 97% requiring a processing time of 2.52 seconds.

Automatic currency recognition and authentication has become an impending challenge today particularly because of the prevailing fraudulent activities as it hampers our economy. According to the RBI report 435,607 fake notes has been detected in year 2010-2011 and the number is only increasing with technological advancements in the field of printing. Image processing techniques such as texture based, pattern or color based, character recognition etc using different operator or tools such as Prewitt or Sobel or Canny edge detector, ANN, heuristic analysis, SVM etc are commonly used for recognition and authentication of paper currency note. Despite several researches it still remains an open challenge. This paper intends to present an extensive survey of the recent technological trends in recognition and authentication of paper currency note while identifying the various challenges.