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
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

NLP and OCR based Automatic Answer Script Evaluation System

by Pranav Deepak, R. Rohan, R. Rohith, Roopa R.
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 42
Year of Publication: 2024
Authors: Pranav Deepak, R. Rohan, R. Rohith, Roopa R.
10.5120/ijca2024924038

Pranav Deepak, R. Rohan, R. Rohith, Roopa R. . NLP and OCR based Automatic Answer Script Evaluation System. International Journal of Computer Applications. 186, 42 ( Sep 2024), 22-27. DOI=10.5120/ijca2024924038

@article{ 10.5120/ijca2024924038,
author = { Pranav Deepak, R. Rohan, R. Rohith, Roopa R. },
title = { NLP and OCR based Automatic Answer Script Evaluation System },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2024 },
volume = { 186 },
number = { 42 },
month = { Sep },
year = { 2024 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number42/nlp-and-ocr-based-automatic-answer-script-evaluation-system/ },
doi = { 10.5120/ijca2024924038 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-09-30T23:02:41.246778+05:30
%A Pranav Deepak
%A R. Rohan
%A R. Rohith
%A Roopa R.
%T NLP and OCR based Automatic Answer Script Evaluation System
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 42
%P 22-27
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Evaluation of answer scripts is a tedious laborious process in the education domain. Proper solution is proposed in this paper using two different state of art technologies i.e., Natural Language Processing (NLP) and Optical Character Recognition (OCR). To develop the Automatic Answer Script Evaluation System. The system is intended to simplify grading by automatic the scoring of written responses in a consistent and accurate way. The NLP portion of the system is responsible for understanding the semantic purposes in textual content of answer scripts. It uses state-of-the-art language models to assess and infer the context, coherence, and entailment properties of the generated text answers. Using NLP to understand text, the system can check not only for correct grammar but also gauge how deeply a particular concept is understood.

References
  1. A. Rokade, B. Patil, S. Rajani, S. Revandkar, and R. Shedge, "Automated Grading System Using Natural Language Processing," in 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, 2018, pp. 1123-1127, doi: 10.1109/ICICCT.2018.8473170.
  2. V.S. Sadanand, K.R. Guruvyas, P.P. Patil, J. Janardhan Acharya, and S. Gunakimath Suryakanth, "An automated essay evaluation system using natural language processing and sentiment analysis," International Journal of Electrical and Computer Engineering (IJECE), 2022.
  3. V. Kumari, P. Godbole, and Y. Sharma, "Automatic Subjective Answer Evaluation," 2023, doi: 10.5220/0011656000003411.
  4. A.K.R. Maya, J. Nazura, and B.L. Muralidhara, "Recent Trends in Answer Script Evaluation – A Literature Survey," 2023, doi: 10.2991/ahis.k.210913.014.
  5. Prof. S.P. Raut, S.D. Chaudhari, V.B. Waghole, P.U. Jadhav, and A.B. Saste, "Automatic Evaluation of Descriptive Answers Using NLP and Machine Learning," 2022, doi: 10.48175/IJARSCT-3030.
  6. S. K. et al., "Automatic Answer Evaluation Using Deep Learning Algorithms," 2022, doi: 10.31838/ecb/2023.12.s3.039.
  7. G. Ng'Ochoi, P. Sijimol, and S. Mariam Varghese, "Grading descriptive answer scripts using deep
  8. learning," International Journal of Innovative Technology and Exploring Engineering, vol. 8, pp. 991-996, 2019.
  9. V. Lakshmi and V. Ramesh, "Evaluating Student's Descriptive Answers Using Natural Language Processing and Artificial Neural Networks," ISSN: 2320-2882, 2017.
  10. N. D. Kamraj, "Survey on Techniques used for Evaluation of Exam Answer Papers," 2020.
  11. M. Sebastian, R. Kunjumon, S. Shaji, and Prof. S. R., "DigiValuate: Answer Sheet Evaluation System using Natural Language Processing," 2021.
  12. S. Mangesh, P. Maheshwari, and A. Upadhyaya, "Subjective Answer Script Evaluation using Natural Language Processing," 2022.
  13. V. Tanwar, "Machine Learning based Automatic Answer Checker Imitating Human Way of Answer Checking," INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT), vol. 10, no. 12, December 2021.
  14. B. S. J. Kapoor, S. M. Nagpure, S. S. Kolhatkar, P. G. Chanore, M. M. Vishwakarma, and R. B. Kokate, "An analysis of automated answer evaluation systems based on machine learning," in 2020 International Conference on Inventive Computation Technologies (ICICT), Feb. 2020, pp. 439–443, doi: 10.1109/ICICT48043.2020.9112429.
  15. M. M. Rahman and F. H. Siddiqui, "NLP-based Automatic Answer Script Evaluation," 2018.
  16. D. Lopresti, "Optical character recognition errors and their effects on natural language processing," IJDAR, vol. 12, pp. 141–151, 2009, DOI: 10.1007/s10032-009-0094-8.
Index Terms

Computer Science
Information Sciences

Keywords

Natural Language Processing OCR Analysis

Powered by PhDFocusTM