Counterfeit notes are one of the biggest problem occurring in cash transactions. For country like India, it is becoming big hurdle. Because of the advances in printing, scanning technologies it is easily possible for a person to print fake notes with use of latest hardware tools. Detecting fake notes manually becomes time-consuming and untidy process hence there is need of automation techniques with which currency recognition process can be efficiently done. Here we implemented our proposed

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... in two ways: one by using analysis through hyper spectral imaging and the second one is the Extraction of different features in fake and real currency notes and through comparing with each other we can able to differentiate the fake note from the real note. We integrated these two systematic approaches in our proposed work. The different colour lights used for hyper spectral imaging are Ultraviolet (UV) light, Normal LED Bulb, Red LED light, Green LED light and Blue LED light with different wavelengths ranging from 360 nm to 800nm respectively. In image processing part, The different features present in the testing input note is identified and compared with the real note and results as the given note is fake or not. Entropy is measured for all the features. The Aspect Ratio is calculated initially for the input notes in order to classify the given note is 100,500 or 2000. This all modules are implemented in MATLAB. We have implemented a fake note detection unit with image processing algorithms. The experimental results indicate that the results achieved are nearly accurate, since the textural background which looks very complex has similar intensity levels.