Abstract
The main purpose of this study is to propose a method that could recognize the Pakistani paper currency note. There are many real-life applications which heavily use many techniques based on Pattern Recognition such as voice recognition, character recognition, handwriting recognition and face recognition. Paper currency recognition is a new application of pattern recognition. This application uses the computing power in differentiating between different kinds of currencies with their suitable class. Selection of proper feature enhanced the performance of the overall system. We are aiming to develop an intelligent system for Pakistani paper currency that could recognize the currency note accurately. In this study, we have taken samples domain of five different Pakistani paper currency notes (Rs. 10, 20, 50, 100, 1000). We scanned total 100 currency notes, 20 from each sample of selected domain for feature extraction of these images using a software. The images will be matched with the features stored in MAT file and if the features of test images will be matched with that file, the software will return the class of that currency note. Experimental results are presented which show that this scheme can recognize currently available 8 notes of Pakistan’s Currency (Rs. 10, 20, 50, 100, 500, 1000 etc.) successfully with an average accuracy of 98.57%.
Highlights
We live in an age of information where everyone is busy and life becoming faster day-by-day
If the features of test image will be matched with the features in MAT file the software will return the class of that currency note
This thesis shows the method for currency recognition using image processing
Summary
We live in an age of information where everyone is busy and life becoming faster day-by-day. The neural network can recognize patterns effectively and robustly In this they use a new kind of banknote Thai bank note as the object of recognition. In this method non masked pixel value of banknotes is computed and feed to the neural network for recognizing paper currency For this two sensors are used at the front and back of paper currency but decision is done by the image of the front. One is for inserting direction and the others are for the face value the distinctive data pattern according to the inserting direction shows relatively clearer tendency than that of the face value With this method, we can get a high recognition rate except for 100 and 200 Euro bank notes. This occurs frequently and more researches will be needed
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