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

A Review on Optical Character Recognition Techniques

by Hiral Modi, M. C. Parikh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 160 - Number 6
Year of Publication: 2017
Authors: Hiral Modi, M. C. Parikh
10.5120/ijca2017913061

Hiral Modi, M. C. Parikh . A Review on Optical Character Recognition Techniques. International Journal of Computer Applications. 160, 6 ( Feb 2017), 20-24. DOI=10.5120/ijca2017913061

@article{ 10.5120/ijca2017913061,
author = { Hiral Modi, M. C. Parikh },
title = { A Review on Optical Character Recognition Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2017 },
volume = { 160 },
number = { 6 },
month = { Feb },
year = { 2017 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume160/number6/27078-2017913061/ },
doi = { 10.5120/ijca2017913061 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:05:57.621770+05:30
%A Hiral Modi
%A M. C. Parikh
%T A Review on Optical Character Recognition Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 160
%N 6
%P 20-24
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

At present scenario, there is growing demand for the software system to recognize characters in a computer system when information is scanned through paper documents. This paper presents detailed review in the field of Optical Character Recognition. Various techniques are determined that have been proposed to realize the center of character recognition in an optical character recognition system. OCR (Optical Character Recognition) translates images of typewritten or handwritten characters into the electronically editable format and it preserves font properties. Different techniques for pre-processing and segmentation have been surveyed and discussed in this paper.

References
  1. A. S. Sawant, “Script Independent Text Pre-processing and Segmentation for OCR,” Int. Conf. Electr. Electron. Signals, Commun. Optim. - 2015, pp. 1–5, 2015.
  2. V. Kieu, F. Cloppet, and N. Vincent, “OCR Accuracy Prediction Method Based on Blur Estimation,” 2016 12th IAPR Work. Doc. Anal. Syst., pp. 317–322, 2016.
  3. J. B. Pedersen, K. Nasrollahi, and T. B. Moeslund, “Quality Inspection of Printed Texts,” IWSSP 2016- 23rd Int. Conf. Syst. Image Process. 23-25 May 2016, Bratislava, Slovakia, pp. 6–9, 2016.
  4. A. F. Mollah, N. Majumder, S. Basu, and M. Nasipuri, “Design of an Optical Character Recognition System for Camera- based Handheld Devices,” IJCSI, vol. 8, no. 4, pp. 283–289, 2011.
  5. B. Jain and M. Borah, “A Comparison Paper on Skew Detection of Scanned Document Images Based on Horizontal and Vertical,” IJSRP, vol. 4, no. 6, pp. 4–7, 2014.
  6. E. N. Bhatia, “Optical Character Recognition Techniques : A Review,” IJARCSSE, vol. 4, no. 5, pp. 1219–1223, 2014.
  7. M. Shen, “Improving OCR Performance with Background Image Elimination,” 2015 12th Int. Conf. Fuzzy Syst. Knowl. Discov., pp. 1566–1570, 2015.
  8. A. Coates et al., “Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning.”
  9. P. Road, “Confidence Guided Progressive Search and Fast Match Techniques for High Performance ChineseEnglish OCR *,” IEEE, pp. 89–92, 2002.
  10. H. Wang and J. Kangas, “Character-Like Region Verification for Extracting Text in Scene Images,” no. 11, 2001.
  11. I. Kastelan, S. Kukolj, V. Pekovic, V. Marinkovic, and Z. Marceta, “Extraction of Text on TV Screen using Optical Character Recognition,” IEEE, pp. 153–156, 2012.
  12. J. Diaz-escobar, “Optical Character Recognition based on phase features,” IEEE, 2015.
  13. A. Thilagavathy, K. Aarthi, and A. Chilambuchelvan, “A Hybrid Approach to Extract Scene Text from Videos,” ICCEET, pp. 1017–1022, 2012.
  14. S. Goyal, “Optical Character Recognition,” IJARCSSE, vol. 3, no. 11, pp. 982–985, 2013.
  15. G. Vamvakas, B. Gatos, N. Stamatopoulos, and S. J. Perantonis, “A Complete Optical Character Recognition Methodology for Historical Documents,” pp. 525–532, 2008.
  16. L. S. Yaeger, B. J. Webb, and R. F. Lyon, “Search for Online , Printed Handwriting N EWTON,” Am. Assoc. Artif. Intell., vol. 19, no. 1, pp. 73–90, 1998.
  17. J. Hu, S. G. Lim, and M. K. Brown, “Writer independent on-line handwriting recognition using an HMM approach,” J. PATTERN Recognit. Soc., vol. 33, pp. 133–147, 2000.
  18. A. Funada, D. Muramatsu, and T. Matsumoto, “The Reduction of Memory and the Improvement of Recognition Rate for HMM On-line Handwriting Recognition,” IEEE, pp. 0–5, 2004.
  19. J. r´ı Matas, “Real-Time Scene Text Localization and Recognition,” IEEE, pp. 3538–3545, 2012.
  20. H. Lin and C. Hsu, “Optical Character Recognition with Fast Training Neural Network,” IEEE, pp. 1458–1461, 2016.
  21. C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, V. Loumos, and E. Kayafas, “A License Plate-Recognition Algorithm for Intelligent Transportation System Applications,” IEEE, vol. 7, no. 3, pp. 377–392, 2006.
  22. Y. J. Zhang, “A survey on evaluation methods for image segmentation,” pp. 1–13.
  23. A. Singh, K. Bacchuwar, and A. Bhasin, “A Survey of OCR Applications,” Int. J. Mach. Learn. Comput., vol. 2, no. 3, pp. 314–318, 2012
  24. .
Index Terms

Computer Science
Information Sciences

Keywords

Character Recognition System Image Segmentation OCR Preprocessing Skew correction Classifier.

Powered by PhDFocusTM