Automated Script Evaluation using Machine Learning and Natural Language Processing

Abstract

Correcting handwritten answer booklets manually can be a challenging task for professors, involving significant time and effort. To address this issue, the paper proposes an automated evaluation system that uses DL and NLP techniques. The suggested approach begins by extracting raw text from image files using a proven GCP OCR text extract model, which is well-known for its better accuracy and efficiency. Furthermore, Natural Language Processing methods like BERT and GPT-3 are used to extract keywords and summarize extensive answers. The suggested technique gives marks that are usually comparable to those issued by manual evaluation. Furthermore, the article suggests a web tool that simplifies the evaluation procedure. The application outputs the raw text of student answers and the answer key, a synopsis of the student’s response, and the marks gained based on the extracted keywords.