Bangladeshi Paper Currency Recognition System Using Supervised Learning

The characteristics of currency note vary from country to country. The recognition of currency depends on the characteristics of currency note of a particular country. Due to use for a long time, currency note may be contaminated by noises. It is difficult for a system to recognize old, torn, and noisy images of currency. This work focuses on Bangladeshi paper currency recognition based on supervised learning. Here a recognition system for Bangladeshi banknotes has been proposed. Initially, it receives the images of Bangladeshi banknotes as input. Then each image is divided into three channels. Filtering is applied to each channel. Finally, the red, the green, and the blue channels are recombined to get back the RGB image. Different features such as HSV, edge, and grey-level co-occurrence matrix are extracted from the RGB image. Total features are stored. Then Euclidean distance of features between the input image and the template images are computed. The minimum distance provides the required output. The performance of the proposed system has been evaluated using a challenging dataset with a wide variety of conditions, such as old, dirty, and torn banknotes.