Cited By
View all- Systems M(2023)RetractedMobile Information Systems10.1155/2023/98601702023Online publication date: 1-Jan-2023
Analysis of Ancient Literary Works Based on Intelligent Image Text Recognition
Author: 
Jia Fan Academic Editor: Chi LinAuthors Info & Claims
Jia Fan Academic Editor: Chi LinAuthors Info & ClaimsAbstract
The analysis of ancient literary works in the era of digital intelligence needs to keep pace with the times. In order to improve the analysis effect of ancient literary works, this paper combines the intelligent image text recognition algorithm to extract the features of ancient literary works and proposes an intelligent algorithm that can be used for the analysis of ancient literary works. Moreover, this paper combines the analysis needs of literary works to improve the algorithm. In order to verify the role of the intelligent image text recognition algorithm proposed in this paper in the analysis of ancient literary works, this paper scans a large number of pictures of ancient literary works in the library by scanning to construct the experimental database of this paper. Finally, this paper combines experimental research to verify the algorithm proposed in this paper. From the experimental results, it can be seen that the method proposed in this paper has a certain effect, and it can be used as a reference for the digital processing and digital preservation of subsequent literary works, and it can also be used as a reference for the management of digital libraries.
References
[1]
J. Zhao, X. Xu, H. Jiang, and Y. Ding, “The effectiveness of virtual reality-based technology on anatomy teaching: a meta-analysis of randomized controlled studies,” BMC Medical Education, vol. 20, no. 1, pp. 127–136, 2020.
[2]
S. J. Bennie, K. E. Ranaghan, H. Deeks, H. E. Goldsmith, M. B. O’Connor, A. J. Mulholland, and D. R. Glowacki, “Teaching enzyme catalysis using interactive molecular dynamics in virtual reality,” Journal of Chemical Education, vol. 96, no. 11, pp. 2488–2496, 2019.
[3]
S. F. M. Alfalah, J. F. M. Falah, T. Alfalah, M. Elfalah, N. Muhaidat, and O. Falah, “A comparative study between a virtual reality heart anatomy system and traditional medical teaching modalities,” Virtual Reality, vol. 23, no. 3, pp. 229–234, 2019.
[4]
M. Reymus, A. Liebermann, and C. Diegritz, “Virtual reality: an effective tool for teaching root canal anatomy to undergraduate dental students–a preliminary study,” International Endodontic Journal, vol. 53, no. 11, pp. 1581–1587, 2020.
[5]
V. L. Dayarathna, S. Karam, R. Jaradat, M. A. Hamilton, M. Nagahi, S. Joshi, and B. Driouche, “Assessment of the efficacy and effectiveness of virtual reality teaching module: a gender-based comparison,” International Journal of Engineering Education, vol. 36, no. 6, pp. 1938–1955, 2020.
[6]
O. Hernandez-Pozas and H. Carreon-Flores, “Teaching international business using virtual reality,” Journal of Teaching in International Business, vol. 30, no. 2, pp. 196–212, 2019.
[7]
V. Andrunyk, T. Shestakevych, and V. Pasichnyk, “The technology of augmented and virtual reality in teaching children with ASD,” Econtechmod: Scientific Journal, vol. 4, no. 7, pp. 59–64, 2018.
[8]
R. Mayne and H. Green, “Virtual reality for teaching and learning in crime scene investigation,” Science & Justice, vol. 60, no. 5, pp. 466–472, 2020.
[9]
C. Yang, S. Huan, and Y. Yang, “A practical teaching mode for colleges supported by artificial intelligence,” International Journal of Emerging Technologies in Learning (IJET), vol. 15, no. 17, pp. 195–206, 2020.
[10]
K. Kim and Y. Park, “A development and application of the teaching and learning model of artificial intelligence education for elementary students,” Journal of The Korean Association of Information Education, vol. 21, no. 1, pp. 137–147, 2017.
[11]
O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, “Systematic review of research on artificial intelligence applications in higher education–where are the educators?” International Journal of Educational Technology in Higher Education, vol. 16, no. 1, pp. 39–27, 2019.
[12]
S. C. H. Yang, W. K. Vong, R. B. Sojitra, T. Folke, and P. Shafto, “Mitigating belief projection in explainable artificial intelligence via Bayesian teaching,” Scientific Reports, vol. 11, no. 1, pp. 1–17, 2021.
[13]
Y. Lee 서울영도초등학교, “An analysis of the influence of block-type programming language-based artificial intelligence education on the learner’s attitude in artificial intelligence,” Journal of The Korean Association of Information Education, vol. 23, no. 2, pp. 189–196, 2019.
[14]
J. M. Alonso, “Teaching explainable artificial intelligence to high school students,” International Journal of Computational Intelligence Systems, vol. 13, no. 1, pp. 974–987, 2020.
[15]
R. Yang, “Artificial intelligence-based strategies for improving the teaching effect of art major courses in colleges,” International Journal of Emerging Technologies in Learning (iJET), vol. 15, no. 22, pp. 146–160, 2020.
[16]
P. Ribeiro, H. Simões, and M. Ferreira, “Teaching artificial intelligence and logic programming in a competitive environment,” Informatics in Education, vol. 8, no. 1, pp. 85–100, 2009.
[17]
V. N. Ganesh, S. G. Acharya, S. Bhat, and S. V. Yashas, “Machine vision robot with real time sensing,” Journal of Advancements in Robotics, vol. 1, no. 3, pp. 30–34, 2020.
[18]
H. H. Chu and Z. Y. Wang, “A study on welding quality inspection system for shell-tube heat exchanger based on machine vision,” International Journal of Precision Engineering and Manufacturing, vol. 18, no. 6, pp. 825–834, 2017.
[19]
N. Noguchi, “Agricultural vehicle robot,” Journal of Robotics and Mechatronics, vol. 30, no. 2, pp. 165–172, 2018.
[20]
V. Villani, F. Pini, F. Leali, C. Secchi, and C. Fantuzzi, “Survey on human-robot interaction for robot programming in industrial applications,” IFAC-PapersOnLine, vol. 51, no. 11, pp. 66–71, 2018.
[21]
S. Huang, K. Shinya, N. Bergström, Y. Yamakawa, T. Yamazaki, and M. Ishikawa, “Dynamic compensation robot with a new high-speed vision system for flexible manufacturing,” International Journal of Advanced Manufacturing Technology, vol. 95, no. 9-12, pp. 4523–4533, 2018.
Index Terms
- Analysis of Ancient Literary Works Based on Intelligent Image Text Recognition
Index terms have been assigned to the content through auto-classification.
Recommendations
Reviving ancient rome: virtual reality at the service of cultural heritage
EuroMed'12: Proceedings of the 4th international conference on Progress in Cultural Heritage PreservationSince the Renaissance, the remains, myths and visual representations of ancient Rome have influenced not only European culture but also the architecture and urban planning of the big cities of Europe and of the Americas through the process of ...
How Can Generative Artificial Intelligence Techniques Facilitate Intelligent Research into Ancient Books?
Generative AI changes the paradigm of natural language processing research, sets off a new trend of research in computational humanities and computational social sciences, and provides unique perspectives on digital intelligence-enabled ancient book ...
Comments
Information & Contributors
Information
Published In
Copyright © 2022 Jia Fan.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher
IOS Press
Netherlands
Publication History
Published: 01 January 2022
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 28 Dec 2024
Other Metrics
Citations
Cited By
View all- Systems M(2023)RetractedMobile Information Systems10.1155/2023/98601702023Online publication date: 1-Jan-2023