Automated Evaluation of Handwritten Answer Script Using Deep Learning Approach

Abstract

Automatic Essay Grading (AEG) is one of the exciting research topics in the field of adopting technology in education. In the education system assessment of student’s answer script is a critical job of teachers; yet doing so consumes a significant amount of their time and prevents them from working on other tasks. In addition, evaluating a large number of exam scripts is error-prone, inefficient, and tedious. Natural Language Processing (NLP), has created such an opportunity to make the computer learn about written text data and make important decisions based on the learned model. Similarly, it is possible to make a computer be able to assess an answering script based on the model used to train our computer to learn about answers to predefined short questions. In this paper, we propose a deep learning architecture with a combination of Con- volutional Neural Network (CNN) and Bidirectional Long Short Term Memory (BiLSTM) which has the ability to perform both handwritten answers recogni- tion and grading them as accurately as a human expert grader.