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

Scene Text Recognition using Artificial Neural Network: A Survey

by Sunil Kumar, Krishan Kumar, Rahul Kumar Mishra
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 6
Year of Publication: 2016
Authors: Sunil Kumar, Krishan Kumar, Rahul Kumar Mishra
10.5120/ijca2016908804

Sunil Kumar, Krishan Kumar, Rahul Kumar Mishra . Scene Text Recognition using Artificial Neural Network: A Survey. International Journal of Computer Applications. 137, 6 ( March 2016), 40-50. DOI=10.5120/ijca2016908804

@article{ 10.5120/ijca2016908804,
author = { Sunil Kumar, Krishan Kumar, Rahul Kumar Mishra },
title = { Scene Text Recognition using Artificial Neural Network: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 6 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 40-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number6/24283-2016908804/ },
doi = { 10.5120/ijca2016908804 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:37:41.801995+05:30
%A Sunil Kumar
%A Krishan Kumar
%A Rahul Kumar Mishra
%T Scene Text Recognition using Artificial Neural Network: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 6
%P 40-50
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays, scene text recognition has become an important emerging area of research in the field of image processing. In image processing, character recognition boosts the complexity in the area of Artificial Intelligence. Character recognition is not easy for computer programs in comparison to humans. In the broad spectrum of things, it may consider that recognizing patterns is the only thing which humans can do well and computers cannot. There are many reasons including various sources of variability, hypothesis and absence of hard-and-fast rules that define the appearance of a visual character. Hence; there is an unavoidable requirement for heuristic deduction of rules from different samples. This review highlights the superiority of artificial neural networks, a popular area of Artificial Intelligence, over various other available methods like fuzzy logic and genetic algorithm. In this paper, two methods are listed for character recognition – offline and online. The “Offline” methods include Feature Extraction, Clustering, and Pattern Matching. Artificial neural networks use the static image properties. The online methods are divided into two methods, k-NN classifier and direction based algorithm. Thus, the scale of techniques available for scene text recognition deserves an admiration. This review gives a detail survey of use of artificial neural network in scene text recognition.

References
  1. Sushil Gangwar, Krishan Kumar, ”3D Face Recognition Based On Extracting PCA Methods”, International Journal of Engineering Research and Applications (IJERA), ISSN: 2248-9622, Vol2, Issue 2, Mar-Apr 2012, pp 693-696.
  2. Amit Choudhary, “A Review of Various Soft Computing Techniques in the Domain of Handwriting Recognition,”, International Journal of Information & Computation Technology. Vol. 4, No. 6, pp. 601-606.
  3. Rahul Malhotra, Narinder Singh and Yaduvir Singh. Soft computing techniques for process control applications. International Journal on Soft Computing (IJSC), Vol.2, No.3 (2011).
  4. A. Thilagavathy, K. Aarthi and A. Chilambuchelvan. Text detection and extraction from videos using ann based network. International Journal on Soft Computing, Artificial Intelligence and Applications. Vol. 1, No.1 (2012), pp.19-28.
  5. Yi-Feng Pan, Xinwen Hou, and Cheng-Lin Liu. A robust system to detect and localize texts in natural scene images. In International Workshop on Document Analysis Systems, 2008.
  6. Yi-Feng Pan, Xinwen Hou, and Cheng-Lin Liu. Text localization in natural scene images based on conditional random field. In ICDAR, 2009.
  7. P. M. Kamble and R. S. Hegadi, “Handwritten marathi basic character recognition using statistical method,” in Emerging Research in Computing, Information, Communication and Applications. Elsevier, 2014, Vol. 3, 2014, pp. 28–33.
  8. Adam Coates, Blake Carpenter, Carl Case, Sanjeev Satheesh, Bipin Suresh, Tao Wang, David J. Wu, and Andrew Y. Ng. Text detection and character recognition in scene images with unsupervised feature learning. In ICDAR, 2011.
  9. V. Sagar and K. Kumar, "A symmetric key cryptography using genetic algorithm and error back propagation neural network," Computing for Sustainable Global Development (INDIACom), 2015 2nd International Conference on, New Delhi, 2015, pp. 1386-1391.
  10. Vikas sagar, Krishan Kumar ”A Symmetric Key Cryptography Using Counter Propagation Neural Network”, International Conference on Information and Communication Technology for Competitive Strategies, ACM-ICPS Proceedings Volume ISBN No 978-1-4503-3216-3.
  11. D. C. Ciresan, U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classication. Technical Report IDSIA-04-12, Dalle Molle Institute for Articial Intelligence, 2012.
  12. Adam Coates, Blake Carpenter, Carl Case, Sanjeev Satheesh, Bipin Suresh, Tao Wang, David J. Wu, and Andrew Y. Ng. Text detection and character recognition in scene images with unsupervised feature learning. In ICDAR, 2011.
  13. Chucai Yi and Yingli Tian. Text Detection in Natural Scene Images by Stroke Gabor Words. 2011 International Conference on Document Analysis and Recognition. 177-181.
  14. Xu-Cheng Yin, Xuwang Yin, Kaizhu Huang, and Hong-Wei Hao. Robust Text Detection in Natural Scene Images. 2013.
  15. Parshuram M. Kamble, Ravinda S. Hegadi. Handwritten Marathi character recognition using R-HOG Feature. Procedia Computer Science 45 (2015) 266 – 274.
  16. Yi-Feng Pan, Xinwen Hou, Cheng-Lin Liu. Text Localization in Natural Scene Images based on Conditional Random Field. 2009 10th International Conference on Document Analysis and Recognition.
  17. A. Thilagavathy, K. Aarthi, A. Chilambuchelvan. Text Detection and Extraction From Videos Using ANN Based Network. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.1, No.1, August 2012.
  18. Xiangrong Chen and A.L. Yuille. Detecting and reading text in natural scenes. In Computer Vision and Pattern Recognition, volume 2, 2004.
  19. Marc'Aurelio Ranzato, Christopher Poultney, S. Chopra, and Y. LeCun. Efficient learning of sparse representations with an energy-based model. In NIPS, 2007.
  20. B. Epshtein, E. Ofek, and Y. Wexler. Detecting text in natural scenes with stroke width transform. In CVPR, 2010.
  21. T. M. Rath and R. Manmatha: Features for Word Spotting in Historical Manuscripts. In: Proc. of the 7th Int'l Conf. on Document Analysis and Recognition (ICDAR), Edinburgh, Scotland, August 3-6, 2003, vol. 1, pp. 218-222.
  22. M.K. Jindal, R.K. Sharma, G.S.Lehal, “Segmentation of Horizontally Overlapping lines in Printed Gurmukhi Script”, IEEE, 2006.
  23. A. Zahour, B. Taconet, L. Sulem, and W. Boussellaa, “Overlapping and multi-touching text line segmentation by Block Covering analysis,” Pattern Analysis and Applications, Vol. 12, pp. 335-351, 2008.
  24. G. Louloudis, et al. Text line and word segmentation of hand written documents. Pattern Recognition, 42, 3169—3183, 2009.
  25. N Stamatopoulos, B Gatos, I Pratikakis, SJ Perantonis. Goal-oriented rectification of camera-based document images. Image Processing, IEEE Transactions on 20 (4), 910-920, 2011.
  26. Yi Li, Yefeng Zheng, David Doermann, Stefan Jaeger,” Script-Independent Text Line Segmentation in Freestyle Handwritten Documents.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 8, Aug.2008.
  27. T Lambrianidis, K Lyroudia, O Pandelidou, A Nicolaou. Evaluation of periapical radiographs in the recognition of C-shaped mandibular second molars. Int Endod J 2001 Vol 34 (458-62)
  28. A. Graves, S. Fern´andez, F. Gomez, and J. Schmidhuber, “Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks,” in ICML, Pittsburgh, USA, 2006.
  29. F. Ahmed and S. Farid, “Application of Niblack’s Method on Images,” International Conference on Emerging Technologies, 2009.
  30. F. Yin, C.L. Liu, Handwritten text line extraction based on minimal spanning tree clustering, Proc. 5th Int. Conf. on Wavelet Analysis and Pattern Recognition, Vol.3, pp. 1123- 1128, 2007.
  31. Xiaojun Du, Wumo Pan, Tien D. Bui,” Text line segmentation in handwritten documents using Mumford–Shahmodel,” Pattern Recognition vol. 42, pp. 3136 – 3145, 2009.
  32. A. Suliman, A. Shakil, M. N. Sulaiman, M. Othman, R. Wirza, Hybrid of HMM and Fuzzy Logic for handwritten character recognition DOI: 10.1109/ITSIM.2008.4631674 Conference: Information Technology, 2008. ITSim 2008. International Symposium on, Volume: 2
  33. Vassilis Papavassilioua, Themos Stafylakis, Vassilis Katsouros, George Carayannis, Handwritten document image segmentatio n into text lines and words, Pattern Recognition 43 (2010) 369—377.
  34. Alireza Alaei, P. Nagabhushan, Umapada Pal. A New Dataset of Persian Handwritten Documents and its Segmentation. 2011 7th Iranian Conference on Machine Vision and Image Processing, MVIP2011-Proceedings 01/2011; DOI: 10.1109/IranianMVIP.2011.6121553.
  35. Brijmohan Singh, Ankush Mittal, M.A. Ansari, Debashis Ghosh. Handwritten Devanagari Word Recognition: A Curvelet Transform Based Approach. International Journal On Computer Science And Engineering (IJCSE) Vol. 3 No. 4 ISSN : 0975-3397 40634.
  36. K. B. M. R. Batuwita, G. E. M. D. C. Bandara. An Improved Segmentation Algorithm for Individual Offline Handwritten Character Segmentation. CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02, 982-988, 2005.
  37. https://books.google.co.in/books?isbn=3662485702.
  38. Mohammad Abu Obaida, Tanay Kumar Roy, Md. Abu Horair, Md. Jakir Hossain. Skew Correction Function of OCR: Stroke-Whitespace based Algorithmic Approach. International Journal of Computer Applications (0975 – 8887), Volume 28– No.8, August 2011.
  39. Bikash Shaw, Swapan Kr. Parui, Malayappan Shridhar. Offline Handwritten Devanagariword Recognition: A Holistic Approach Based On Directional Chain Code Feature And HMM. International Conference On Information Technology IEEE ICIT 978-0-7695-3513-5/08 2008.
  40. Yi-Feng Pan, Xinwen Hou, Cheng-Lin Liu. A Hybrid Approach to Detect and Localize Texts in Natural Scene Images. Chinese Academy of Sciences (CASIA), 20(3):800-13. DOI: 10.1109/TIP.2010.2070803.
  41. Abhishek arvind gulhane. Noise Reduction of an Image by using Function Approximation Techniques. 2709 (2004).
  42. Youssef Bassil, Mohammad Alwani, Ocr post-processing error correction algorithm using google's online spelling suggestion. Journal of Emerging Trends in Computing and Information Sciences, ISSN 2079-8407, Vol. 3, No. 1, January 2012.
  43. Dhaval Salvi, Jun Zhou, Jarrell Waggoner, and Song Wang. Handwritten Text Segmentation using Average Longest Path Algorithm. Ijarcsse, Vol. 3(5), 2008.
  44. T.Saoi, H. Goto, H. Kobayashi, Text detection in color scene images based on unsupervised clustering of multi-channel wavelet features, in: Eighth International Conference on Document Analysis and Recognition (ICDAR'05), vol.2, 2005, pp. 690–694.
  45. S.A. Angadi, M.M. Kodabagi, A texture based methodology for text region extraction from low resolution natural scene images, in: Advance Computing Conference, 2010, pp. 121–128.
  46. J. Gllavata, R. Ewerth, B. Freisleben, Text detection in images based on unsupervised classification of high-frequency wavelet coefficients, in: Proceedings of the17th International Conference on Pattern Recognition, ICPR 2004,IEEE, vol.1, 2004, pp. 425–428.
  47. J. Liu, C. Wang, An algorithm for image binarization based on adaptive threshold, in: 2009 Chinese Control and Decision Conference, CCDC'09, IEEE, 2009, pp. 3958–3962.
  48. P. Shivakumara, T. Phan, C. Tan, A Laplacian approach to multi-oriented text detection in video, IEEE Trans. Pattern Anal. Mach. Intell. 33 (2) (2011) 412–419.
  49. Jerod J. Weinman, Erik Learned-Miller, and Allen R. Hanson. A discriminative semimarkov model for robust scene text recognition. In Proc. IAPR International Conference on Pattern Recognition, Dec. 2008.
  50. L. Neumann and J. Matas. A method for text localization and recognition in real-world images. In AACCV, 2010.
  51. K. Wang, B. Babenko, and S. Belongie. End-to-end scene text recognition. In ICCV, 2011.
  52. Attaullah Khawaja, Shen Tingzhi, Noor Mohammad Memon, AltafRajpa, “Recognition of printed Chinese characters by using Neural Network”, 1-4244-0794-X/06/$20.00 ©2006 IEEE, pp 169-172.
  53. R.O. Duda, P.E. Hart, and D.G. Stork. Pattern Classification. Wiley, 2001.
  54. Jerod Weinman, Erik Learned-Miller, and Allen R. Hanson. Scene text recognition using similarity and a lexicon with sparse belief propagation. In Transactions on Pattern Analysis and Machine Intelligence, volume 31, 2009.
  55. E.Kavallieratos, N.Antoniades, N.Fakotakis and G.Kokkinakis, “Extraction and recognition of handwritten alphanumeric characters from application forms”.
  56. X. Fan and G. Fan. Graphical Models for Joint Segmentation and Recognition of License Plate Characters. IEEE Signal Processing Letters, 16(1), 2009.
  57. Jerod Weinman, Erik Learned-Miller, and Allen R. Hanson. Scene text recognition using similarity and a lexicon with sparse belief propagation. In Transactions on Pattern Analysis and Machine Intelligence, volume 31, 2009.
  58. Yuk Yirtg Chung, M„an To Wong, “Handwritten Character Recognition By Fourier Descriptors And Neural Network”, 1997 IEEE TENCON, pp 391-394.
  59. Rókus Arnold, Póth Miklós, “Character Recognition Using Neural Networks”, CINTI 2010, 978-1-4244-9280-0/10/$26.00 ©2010 IEEE, 311-314.
  60. Suruchi G. Dedgaonkar, Anjali A. Chandavale, Ashok M. Sapkal. Survey of Methods for Character Recognition. International Journal of Engineering and Innovative Technology (IJEIT), Volume 1, Issue 5, May 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Character Recognition Scene text recognition Text extraction Feature extraction Artificial Neural Network.

Powered by PhDFocusTM