Improved Indian currency recognition: neighbourhood-centred image processing and CNNs with region of pixel selection techniques

The paper proposes an improved approach for Indian currency recognition using neighbourhood-centred image processing and convolutional neural networks (CNNs) with region of pixel selection techniques. The method includes image pre-processing steps such as noise reduction, contrast enhancement, and resizing. A neighbourhood-centred image processing technique is applied to capture contextual information from local neighbourhoods around each pixel. A CNN-based model is then trained on the pre-processed images to learn discriminative features for currency recognition. To enhance accuracy and efficiency, a region of pixel selection technique is introduced to select only relevant regions of interest for CNN training and inference, reducing computational overhead. Experimental results demonstrate the effectiveness of the proposed approach, achieving high accuracy in currency recognition and improved efficiency in terms of computational time and memory requirements. The proposed method has potential applications in automated cash-handling machines, vending machines, and mobile payment systems where reliable currency recognition is essential.