A Deep Learning Ensemble with OCR Tools for PAN Card Recognition System

The growing demand for efficient and automated identity verification has led to significant progress in document recognition systems. In this paper, A Deep Learning Ensemble is Proposed, combined with Optical Character Recognition (OCR) Tools specifically for PAN Card Recognition, with the goal of enhancing the accuracy and efficiency of information extraction from PAN cards, which are essential for financial and administrative transactions in India. Challenges such as low image quality, variations in text fonts, and the existence of counterfeit documents are addressed. The methodology includes image acquisition, preprocessing (such as noise reduction, contrast enhancement, and background removal), and feature extraction through Convolutional Neural Networks (CNNs) for classification, alongside OCR for text extraction. Furthermore, K-Nearest Neighbors (KNN) is employed for character segmentation, while an error-handling mechanism is implemented to ensure robustness against image distortions and inconsistencies. The system was trained and tested on a varied dataset of PAN card images, encompassing both authentic and counterfeit samples, to bolster fraud detection capabilities. Experimental findings indicate a 93% accuracy rate for standard PAN card images and an accuracy range of 84–100% for distorted images, validating the system's effectiveness in practical scenarios. The combination of deep learning models with OCR is found to significantly accelerates document verification, making it ideal for use in banking, taxation, and digital identity management.