Image-Based Grammar Mistakes Correction Tool

Abstract

"Addressing the inefficiencies in the traditional education system, this project focuses on the burdensome manual assessment of handwritten assignments and the delayed feedback to students. The lack of accessible tools for pre-submission error checks hampers students' skill development. To overcome these challenges, the project proposes an innovative solution utilizing Optical Character Recognition (OCR) and Natural Language Processing (NLP) technologies. The objective is to automate grammar checks in handwritten text, ensuring accuracy and expediting feedback for an enhanced teaching and learning experience. To delve into the specifics, The author employs OCR and NLP capabilities to revolutionize the correction process. Text extraction from handwritten submissions is followed by a rigorous grammar analysis, promising an efficient and accurate correction mechanism. Transitioning to the initial results discussion, The author incorporates pertinent quantitative metrics. For a Machine Learning (ML) classification scenario, metrics like the Confusion Matrix and AUC-ROC offer insights. In regression contexts, the use of metrics such as RMSE (Root Mean Squared Error) and MSE (Mean Squared Error) provides a quantitative measure of this project's performance. These metrics serve as indicators of the project's effectiveness in addressing identified problems. "