Abstract:

This paper introduces an advanced computer vision framework designed to detect alterations in PAN (Permanent Account Number) cards, serving as a robust checkpoint for enterprises. Beginning with an initial phase, the methodology utilizes OpenCV to convert images into grayscale, optimizing computational processing and enhancing interpretability. Central to the framework's effectiveness is the seamless integration of the thresholding function, a crucial step that converts grayscale representations into binary equivalents, facilitating contour extraction necessary for shape analysis and recognition. The subsequent evaluation of image fidelity employs Structural Similarity (SSIM) metrics, a sophisticated methodology adept at identifying potential tampering instances. Refinement of the analytical process involves establishing precise thresholds and extracting contours judiciously, thereby enhancing the framework's discernment capabilities in shape analysis. The project culminates in a visual representation, presenting enriched images delineating contours, effectively identifying discrepancies and strengthening document authentication protocols. In summary, this research exemplifies a sophisticated fusion of computer vision techniques tailored for tamper detection in PAN cards. From initial preprocessing to visually enhanced outputs, each aspect of the framework demonstrates a meticulous approach to bolstering organizational security and ensuring document integrity

Description:

Computer vision, PAN card, Tamper detection, Image processing, Structural Similarity, Contour extraction