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Digital Handwritten Answer Sheet Evaluation System

by Madhavi Kulkarni, Gayatri Adhav, Kunal Wadile, Vrushali Deshmukh, Rohini Chavan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 16
Year of Publication: 2024
Authors: Madhavi Kulkarni, Gayatri Adhav, Kunal Wadile, Vrushali Deshmukh, Rohini Chavan
10.5120/ijca2024923540

Madhavi Kulkarni, Gayatri Adhav, Kunal Wadile, Vrushali Deshmukh, Rohini Chavan . Digital Handwritten Answer Sheet Evaluation System. International Journal of Computer Applications. 186, 16 ( Apr 2024), 9-13. DOI=10.5120/ijca2024923540

@article{ 10.5120/ijca2024923540,
author = { Madhavi Kulkarni, Gayatri Adhav, Kunal Wadile, Vrushali Deshmukh, Rohini Chavan },
title = { Digital Handwritten Answer Sheet Evaluation System },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2024 },
volume = { 186 },
number = { 16 },
month = { Apr },
year = { 2024 },
issn = { 0975-8887 },
pages = { 9-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number16/digital-handwritten-answer-sheet-evaluation-system/ },
doi = { 10.5120/ijca2024923540 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-04-27T03:06:46.081425+05:30
%A Madhavi Kulkarni
%A Gayatri Adhav
%A Kunal Wadile
%A Vrushali Deshmukh
%A Rohini Chavan
%T Digital Handwritten Answer Sheet Evaluation System
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 16
%P 9-13
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The world is moving towards computerization. Manually checking the answer sheets takes a lot of time and effort from the schoolteachers and college professors. To address this challenge, our project aims to streamline the evaluation process by converting handwritten student responses into digital text and comparing them with predetermined model answers provided by educators. This is made possible through the integration of cutting-edge technologies such as optical character recognition (OCR), natural language processing (NLP), and machine learning algorithms. By the utilization of advanced BERT (Bidirectional Encoder Representations from Transformers) models and cosine similarity algorithms, our system ensures accuracy and efficiency when evaluating student answers. Rather than focusing on answer length, the project's main goal is to optimize mark distribution based on key terms. This will save educators time and effort while advancing a fair and uniform evaluation process. Additionally, this approach helps students understand concepts more clearly and motivates them to give exact and accurate answers, which helps produce results that are fair and equal.

References
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Index Terms

Computer Science
Information Sciences

Keywords

OCR Handwritten Answer Sheet Evaluation NLP Machine Learning Evaluation Result.

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