CHEATING RECOGNITION IN PAPER EXAMS USING CV

Abstract

With the shift from exams to electronic examinations, to pen and paper (paper exams), concerns were raised about whether this would make cheating easier. Cheating and academic dishonesty have always been disturbing practice in an academic setting, it kills the creativity of a student. Roughly speaking all teachers meet a high rate of academic dishonesty among their students. This article explores how teachers and students perceive differences in the ease of cheating during written exams, especially paper exams. Nowadays we have control systems that detect cheating and abnormal behaviors during exams. Despite early controls determining cheating during the checking of exam papers is also a great idea. Manually checking each work will take up most of the time and energy, which is also difficult to identify plagiarism. That’s why the paper gives using Computer Vision to optimize checking paper exams and detect cheating levels among students.
Keywords: Paper examinations, PyTesseract, OCR, text recognition, cheating, document image analysis (DIA).