An Examination Application For Blind Students With Subjective Answer Evaluator

Abstract

We present in this paper an examination application for blind students with a subjective answer evaluator. In the present scenario, Blind students need a volunteer to give exams, but we have proposed a solution to that by developing a completely voice-controlled website that also records answers given by the students. This will help increase the number of literates who are visually impaired giving as they can independently give exams. The current way of checking subjective answers is adverse. Whenever a human being evaluates papers, the quality is affected by emotion. In this paper, we are testing four different models for subjective answers evaluation using machine language. These models include Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbours. After testing Random Forest proved to be the best giving 83%accuracy.