A Novel Approach for Exam E-assessment Utilizing Image Processing

Abstract

There is a brand-new feature called Exam (Infinity Exam) that supports paper-based exams and speeds up the entire process while maintaining all of their beneficial qualities and minimizing their drawbacks, notably in higher education. The method is very different from those employed in the earlier 10+ years, which were implemented in a way that prevented them from replicating and supplanting the conventional paper-based examination format. The article's core relies on the image processing flow, which is the most crucial component of the software. Multiple Choice Questions (MCQ) have been a more common method of testing someone's knowledge over time. The use of multiple choice questions in exams is becoming more widespread in the education sector (including in schools and colleges). It is employed even when conducting interviews. The current scenario involves either manually correcting the test or using OMR technology. Having OMR at all times in real time is rather challenging, and manually correcting it takes a lot of effort and could result in a mistake. We address this issue by applying a digital image processing technique in our proposed system to correct the response using multiple-choice questions written in Python. Here, we are processing data using Open-Source Computer Vision Library (OpenCV).