Automatic Quiz Generator

Authors

  • Vaibhav Gupta Student, Department of Computer Science and Engineering , Babu Banarasi Das Institute of Technology & Management, Lucknow, India
  • Hemlata Pant Assistant Professor, Department of Computer Science and Engineering , Babu Banarasi Das Institute of Technology & Management, Lucknow, India
  • Arjun Chaurasia Student, Department of Computer Science and Engineering , Babu Banarasi Das Institute of Technology & Management, Lucknow, India
  • Astha Dwivedi Student, Department of Computer Science and Engineering , Babu Banarasi Das Institute of Technology & Management, Lucknow, India
  • Shubham Singh Student, Department of Computer Science and Engineering , Babu Banarasi Das Institute of Technology & Management, Lucknow, India
  • Pragati Singh Student, Department of Computer Science and Engineering , Babu Banarasi Das Institute of Technology & Management, Lucknow, India

Keywords:

MCQ generation, Natural Language Processing, Deep Learning, Online text, Text image

Abstract

Multiple Choice Questions for any text image, either from long boring books or just some random hand written notes of yours. These questions are not from only that text image but also from internet, so that you can also encounter new questions i.e. extra facts, and who denies extra knowledge. Further it helps to revise and keep you up to date with your learning interests. In this paper we have aggregated different natural language processing technologies (NLP) and implemented them by their effective use for our project Automatic Quiz Generator in which text image is to be uploaded on API we provide so that the user gets the Multiple Choice Questions (MCQs) by searching online. We shall achieve that with the help of OCR, Keyword Extraction, Web Scraping, Django and different NLTK (Natural Language Tool Kit) tool.

Downloads

Download data is not yet available.

Downloads

Published

2021-07-07

How to Cite

[1]
V. Gupta, H. Pant, A. Chaurasia, A. Dwivedi, S. Singh, and P. Singh, “Automatic Quiz Generator”, IJRESM, vol. 4, no. 7, pp. 54–56, Jul. 2021.

Issue

Section

Articles

Most read articles by the same author(s)