CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
Implementation of Feature Selection Using Correlation Matrix in Python

Ahmad Farhan AlShammari
Random Articles
Reseach Article

Digital Handwritten Answer Sheet Evaluation System

by Madhavi Kulkarni, Gayatri Adhav, Kunal Wadile, Vrushali Deshmukh, Rohini Chavan
journal cover thumbnail

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
  1. Muhammad Farrukh Bashir1, Hamza Arshad1, Abdul Rehman Javed 2,(Member, IEEE), Natalia Kryvinska3 And Shahab S. Band 4. (Senior Member, IEEE) "Subjective Answers Evaluation Using Machine Learning and Natural Language Processing" IEEE, December 7, 2021.
  2. Pranali Nikam, Mayuri Shinde, Rajashree Mahajan and Shashikala Kadam "Automatic Evaluation of Descriptive Answer Using Pattern Matching Algorithm" IJCSE, January 31, 2015.
  3. Sijimol P J, Surekha Mariam Varghese "Handwritten Short Answer Evaluation System(HSAES)" IJSRST, February 28, 2018.
  4. Senthilkumar K, Aroabinesh J, Gowtham T, Manikandan K "Automatic answer evolution using deep learning algorithms" ECB, MARCH 15, 2023.
  5. Madhavi B. Desai, Visarg D. Desai, Rahul S. Gupta, Deep D. Mevada, and Yash S. Mistry “A survey on automatic subjective answer evolution” Advances and Applications in Mathematical Sciences, September 11, 2021.
  6. Mandada Samemi, Tirumala Sai Hareesha, Gudluru Venkata Siva Sai Avan Kumar, Nalluri Pramod "Automatic answer evolution using machine learning”, UGC,2022.
  7. Madhavi Kulkarni, Lingayat Mayuri "Effective Product Ranking Method based on Opinion Mining" International Journal of Computer Applications (120- 18),2015.
  8. Smith, J., Johnson, A., & Williams, B. Challenges in Manual Grading of Handwritten Answer Sheets: A Review. “Journal of Educational Assessment”, 12(3), 145-162,(2017).
  9. Jura Sky, D., & Martin, J. H. “Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition” (3rd ed.). Pearson Education, (2019).
  10. Kim, S., & Lee, J. Automated Grading of Handwritten Answer Sheets Using OCR and NLP. “Journal of Educational Technology, 28(1), 78-92, (2022).
  11. Li,M., etal. Integrating OCR and NLP for Automated Assessment of Handwritten Responses. “International Conference on Educational Technology”, 132-145, (2021).
  12. Liu, Y., et al. Deep Learning Approaches for Automated Grading of Mathematical Expressions. “IEEE Transactions on Learning Technologies”, 13(5), 1138-1152, (2020).
  13. Wang, Q., et al. Semantic Similarity-based Scoring of Short Answer Questions. “International Conference on Artificial Intelligence in Education”, 265-278, (2018).
  14. Jones, A., & Brown, B. Challenges in Handwritten Answer Sheet Evaluation: A Review. “Journal of Educational Technology”, 25(2), 145- 162, (2019).
Index Terms

Computer Science
Information Sciences

Keywords

OCR Handwritten Answer Sheet Evaluation NLP Machine Learning Evaluation Result.

Powered by PhDFocusTM