Currency Recognition using SIFT algo.pptx
- 1. “Currency Recognition System using SIFT Feature Descriptor” SUBMITTED BY – 1. SHUBHAM NIJHAWAN (44614802817) 2. DAKSH AHUJA (45814802818) 3. UTKARSH KUMAR (45914802818)
- 2. OBJECTIVE: We are proposing a system for automated currency recognition. Our method can be used for recognizing both the country of origin as well as the denomination and value of a given banknote. Only paper currencies have been considered. This method works by first identifying the country of origin using certain predefined areas of interest, and then extracting the denomination value using characteristics such as size, colour, or text on the note, depending on how much the notes within the same country differ. We will also use this to check whether the currency is real or fake. It is difficult for people (eg. who work for the money exchanging) to recognize currencies from different countries. Our aim is to help people figure out this problem.
- 3. REQUIREMENTS: MATLAB PYTHON JUPYTER NOTEBOOK PYTHON LIBRARIES SUBLIME TEXT CURRENCIES OF DIFFERENT DENOMINATIONS
- 4. SIFT ALGORITHM The Scale-Invariant feature transform(SIFT) is an algorithm to detect and describe local features in images. For any object is an image ,interesting points on the object can be extracted to provide a “feature description” of the object. This description, extracted from a training image, can then be used to identify the object when attempting to locate the object in a test image containing many other objects.
- 5. Input image Read the image Image preprocessing Image segmentation Color Detection Identification of the country Pattern Matching and edge detection Output
- 7. DATA FLOW DIAGRAM The methodology of this project is extracting unique features of the currency note using grid. Grid divides the currency into nine parts on each side which helps to reduce the time complexity of the proposed model. Applying the preprocessing to each block to recognize and extract potential feature of currency note.
- 8. CODE: In this module we are provided with few datasets (currency notes) and further we are analyzing that the inputs that we have taken are converted into gray scale and differentiated as per the key points like color detection,identification of the country,size etc in the pre-processing phase .Further,the currency taken is compared with the datasets present in the system and finally, the output will be the currency which has the maximum match from all the datasets provided.
- 12. FUTURE PERSPECTIVE # All Currency Detection: Being rational, we are going to upgrade our project in a way that it will be able to check for other currencies as well apart from Indian currency. # Making an algorithm that will provide the Indian value of currency : We are a going to create a way in which the given currency denomination will be converted into its Indian value.