Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 5th International Conference on Computing and Data Science

Series Vol. 17 , 23 October 2023


Open Access | Article

Optical character recognition with different languages

Yifan Wu 1 , Yuxi Zhang * 2
1 University of Wuxi Taihu
2 University of Manchester

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 17, 60-64
Published 23 October 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Yifan Wu, Yuxi Zhang. Optical character recognition with different languages. ACE (2023) Vol. 17: 60-64. DOI: 10.54254/2755-2721/17/20230914.

Abstract

Optical character recognition is the combination of optical technology and computer technology to identify text in an image and then recognize the text content in the image, providing individuals with a great deal of ease in their daily lives. Document text recognition, natural scene text recognition, bill text recognition, and ID card recognition have been used in daily life, but there are still many factors that lead to inaccurate identification and detection. Therefore, different texts, patterns or characters are suitable for different types of Optical character recognition. In this paper, we can learn about the Optical character recognition operation methods and find the similarities and differences through researching the technical routes and four different types of Optical character recognition. In addition, by comparing the Optical character recognition of several commonly used languages, the advantages and disadvantages of each method can be analysed.

Keywords

optical character recognition, different languages, advanced technology

References

1. Ptucha, R., Such, F. P., Pillai, S., Brockler, F., Singh, V., & Hutkowski, P. (2019). Intelligent character recognition using fully convolutional neural networks. Pattern recognition, 88, 604-613.

2. V. Jayasundara, S. Jayasekara, H. Jayasekara, J. Rajasegaran, S. Seneviratne and R. Rodrigo, 2019 "TextCaps: Handwritten Character Recognition with Very Small Datasets," IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, pp. 254-262, doi: 10.1109/WACV.2019.00033.

3. H. Zhang, J. Guo, G. Chen and C. Li, 2009 "HCL2000 - A Large-scale Handwritten Chinese Character Database for Handwritten Character Recognition," 2009 10th International Conference on Document Analysis and Recognition, Barcelona, Spain, pp. 286-290, doi: 10.1109/ICDAR.2009.15.

4. Huang Libo, Ling Yongquan. 2021 A parameter-free local linear discriminant analysis method[J]. Computer Science and Applications, 11(4): 1042-1052. https://doi.org/10.12677/CSA.2021.114107

5. J. Memon, M. Sami, R. A. Khan and M. Uddin, "Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)," in IEEE Access, vol. 8, pp. 142642142668, 2020, doi: 10.1109/ACCESS.2020.3012542.

6. Rahiman, M.A. and Rajasree, M.S. (2009) “A detailed study and analysis of OCR Research in South Indian scripts,” 2009 International Conference on Advances in Recent Technologies in Communication and Computing [Preprint]. Available at: https://doi.org/10.1109/artcom.2009.45.

7. Seethalakshmi, R. et al. (2005) “Optical character recognition for printed Tamil text using Unicode,” Journal of Zhejiang University-SCIENCE A, 6(11), pp. 1297–1305. Available at: https://doi.org/10.1631/jzus.2005.a1297.

8. N. Mezghani, A. Mitiche and M. Cheriet, 2002, "On-line recognition of handwritten Arabic characters using a Kohonen neural network," Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition, Niagra-on-the-Lake, ON, Canada, pp. 490-495, doi: 10.1109/IWFHR.2002.1030958.

9. Pechwitz, M., Maddouri, S. S., Märgner, V., Ellouze, N., & Amiri, H. (2002, October). IFN/ENIT-database of handwritten Arabic words. In Proc. of CIFED (Vol. 2, pp. 127-136). Citeseer.

10. Graves A, Schmidhuber J. 2008 Offline handwriting recognition with multidimensional recurrent neural networks[J]. Advances in neural information processing systems, 21.

11. C. Boufenar and M. Batouche, 2017 "Investigation on deep learning for off-line handwritten Arabic Character Recognition using Theano research platform," 2017 Intelligent Systems and Computer Vision (ISCV), Fez, Morocco, pp. 1-6, doi: 10.1109/ISACV.2017.8054902.

12. R. A. Khan, A. Meyer, H. Konik and S. Bouakaz, "Pain detection through shape and appearance features," 2013 IEEE International Conference on Multimedia and Expo (ICME), San Jose, CA, USA, pp. 1-6, doi: 10.1109/ICME.2013.6607608.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 5th International Conference on Computing and Data Science
ISBN (Print)
978-1-83558-025-7
ISBN (Online)
978-1-83558-026-4
Published Date
23 October 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/17/20230914
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated