Fraud Detection in Examination from Hall Ticket

I This paper presents a system designed to detect fraud in the hall ticket verification process of offline examinations. The system employs computer vision and machine learning techniques such as Haar Cascade, grayscale conversion, feature extraction, and cascade classifiers to automate and enhance the verification process. Through real-time detection, the system ensures that only legitimate candidates gain access to the examination hall, thereby reducing potential cheating and impersonation. The methodology includes stages like input acquisition, preprocessing, feature extrac tion, segmentation, and classification, culminating in an accurate result that high lights fraudulent attempts