Classify Currency Attestation For Free

Basically currency is scanned for obtaining image, pre-processing operations are performed which include blurring, noise removal etc. Then the boundary detection and cropping is completed so the feature extraction is the last process after which the result is displayed.

Currency - Banknotes in India are currently being issued in the denomination of Rs 10, Rs 20, Rs 50, Rs 100, Rs 200, Rs 500 and Rs 2000.

The Identification Mark is a special feature in intaglio printing located on the left-hand side middle portion of the banknote. The shapes are different for each denomination of Indian currency and aid the visually impaired in identifying the currency value.

The currency detection using image processing was implemented on MATLAB. Features of currency note like serial number, security thread, Identification mark, Mahatma Gandhi portrait were extracted. The process starts from image acquisition to calculation of intensity of each extracted feature.

The dataset is comprehensive which consist of 3000 high-quality images of 15 different classes. The dataset consist of old as well as new denominations of Indian banknotes. This dataset is useful to build applications of Indian and Thai banknotes classification and detection.

It involves extraction of invisible and visible features of Indian currency notes. This approach consists of different steps like image acquisition, edge detection, gray scale conversion, feature extraction, image segmentation and decision making [5-6].

The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. Machine learning models use a set of input values to predict output values.

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, color, or text on the note, depending on how much the notes within the same country differ.