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. "
