Exam Eval: An Automated Model for Correcting Exam Papers Using OCR and Text Summarization

Jami Satya  Vaishnavi; Koduri  Aparna

Abstract

In the realm of education, grading and correcting exam papers can be a time-consuming and often daunting task for teachers. To streamline this process and provide more efficient feedback to students, this project aims to develop a model that leverages advanced techniques in natural language processing (NLP) and computer vision. The primary objective is to correct exam papers by summarizing the handwritten answers provided by students, incorporating Optical Character Recognition (OCR) through OpenCV for handling handwritten scripts. This model addresses the challenge of grading handwritten scripts by automatically converting the handwritten text into a machine-readable format and generating concise summaries that align with the corresponding teacher's answer summary. By harnessing OCR capabilities, the model extracts text from handwritten images and preprocesses it to improve readability and accuracy. Subsequently, text summarization techniques are applied to condense the extracted text into coherent and informative summaries. The integration of OCR with text summarization provides an efficient and automated solution for correcting handwritten exam papers, ultimately saving time and effort for teachers. This model aims to enhance the grading process, provide consistent and unbiased evaluations, and offer valuable insights into students' performance by matching their answer summaries with those prepared by the teacher.

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