Automating the grading of handwritten examinations through the integration of optical character recognition and machine learning algorithms

Abstract

Advanced OCR and machine learning techniques are exploited in developing an efficient system with accuracy in grading the handwritten assignments and exams. Digitization of input through OCR is used to evaluate student submissions against predefined criteria to reduce human biases and errors in order to accelerate the process of grading. Such key innovations include trained models for the detection and evaluation of a block diagram and diagrams, that facilitate the auto-recognition and assessment of complicated visual content, such as mathematical calculations and graphical representations. The system is adaptive to diverse writing styles, conducts contextual understanding for complex answers, and immediately returns feedback to instructors in line with student performance trends and standards set upon customized grading practices. This comprehensive solution streamlines the otherwise laborious grading process and opens doors to an efficient, objective, and versatile framework for assessing education.