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Reseach Article

Understanding Textbook of History with Pictorial Representation using NLP

by Abhishek Mukherjee, Nikhar Jain, Pratik Nandurkar, Pooja Vengurlekar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 50
Year of Publication: 2019
Authors: Abhishek Mukherjee, Nikhar Jain, Pratik Nandurkar, Pooja Vengurlekar
10.5120/ijca2019919153

Abhishek Mukherjee, Nikhar Jain, Pratik Nandurkar, Pooja Vengurlekar . Understanding Textbook of History with Pictorial Representation using NLP. International Journal of Computer Applications. 178, 50 ( Sep 2019), 8-10. DOI=10.5120/ijca2019919153

@article{ 10.5120/ijca2019919153,
author = { Abhishek Mukherjee, Nikhar Jain, Pratik Nandurkar, Pooja Vengurlekar },
title = { Understanding Textbook of History with Pictorial Representation using NLP },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2019 },
volume = { 178 },
number = { 50 },
month = { Sep },
year = { 2019 },
issn = { 0975-8887 },
pages = { 8-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number50/30889-2019919153/ },
doi = { 10.5120/ijca2019919153 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:53:37.269844+05:30
%A Abhishek Mukherjee
%A Nikhar Jain
%A Pratik Nandurkar
%A Pooja Vengurlekar
%T Understanding Textbook of History with Pictorial Representation using NLP
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 50
%P 8-10
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Every day the number data is generated on the internet. It is very challenging to determine the importance of the content. Text picturing helps a lot to understand the concept related to the text. Text picturing is a cognitive aid that can help with text understanding, as it helps users decide if the text deserves closer look by showing relevant pictures along with the text. The writers, bloggers, and editorial person link the text with media content is crucial. Visual content helps readers follow the root of a discussion or identify the core theme of an article. In efforts to arrange and display helpful images, content managers, news writers, and user-interface designers perform page layout, assigning images to concepts. To improve image-retrieval accuracy, a number of issues must be addressed. Readers of businesses reports, newspapers, and social media face the challenge of interpreting large volumes of text in a short amount of time. Students, Teachers, Professors or History enthusiast face the challenge to identify large data in a short amount of time. This can lead to loss of important information. Survey shows that images related to such topics are helpful for the reader to understand and remember the context for a better period of time. There are also people who are not able to take in written matter at a standard pace images sure can boost their grasping ability. This work will help to resolve such cases. The user can easily identify the concept according to the images rather than the text.

References
  1. S. Oviatt, “Human-Centered Design Meets Cognitive Load Theory: Designing Interfaces that Help People Think,” Proc. 14th ACM Int’l Conf. Multimedia (MM), 2006, pp. 871–880.
  2. A. Göker et al., “Expeditions through Image Jungles: The Commercial Use of Image Libraries in an Online Environment,” J. Documentation, vol. 72, no. 1, 2016, pp. 5–23.
  3. C. White, L. Breslow, and D. Hastings, “Exploring Visual Literacy as a Global Competency: An International Study of the Teaching and Learning of Communication,” Proc. 2015 Int’l Conf. Interactive Collaborative Learning (ICL), 2015, pp. 771–778.
  4. B.J. Baars and N.M. Gage, Fundamentals of Cognitive Neuroscience: A Beginner’s Guide, Academic Press, 2012.
  5. J. Johnson, Designing with the Mind in Mind: Simple Guide to Understanding User Interface Design Guidelines, Morgan Kaufman, 2010.
  6. V. Vandeghinste, I.S.L. Sevens, and F. Van Eynde, “Translating Text intoPictographs,” Natural Language Eng., vol. 23, no. 2, 2017, pp. 217–244.
  7. A.M. Cook and J.M. Polgar, Assistive Technologies: Principles and Practice, Mosby,2015.
  8. M.B. McGrath and J.R. Brown, “Visual Learning for Science and Engineering,” IEEE Computer Graphics and Applications, vol. 25, no. 5, 2005, pp. 56–63.
  9. D. Joshi, J.Z. Wang, and J. Li, “The Story Picturing Engine—A System for Automatic Text Illustration,” ACM Trans. Multimedia Computing, Communications, and Applications, vol. 2, no. 1, 2006, pp. 68–89.
  10. K. Bontcheva and D. Rout, “Making Sense of Social Media Streams through Semantics: A Survey,” Semantic Web, vol. 5, 2014, pp. 373–403.
Index Terms

Computer Science
Information Sciences

Keywords

History Natural Language Processing