A Detailed Analysis of Optical Character Recognition Technology

Authors

  • Karez HAMAD
  • Mehmet KAYA

DOI:

https://doi.org/10.18100/ijamec.270374

Keywords:

OCR, OCR Challenges, OCR Phases, OCR Applications, OCR History

Abstract

In many different fields, there is a high demand for storing information to a computer storage disk from the data available in printed or handwritten documents or images to later re-utilize this information by means of computers. One simple way to store information to a computer system from these printed documents could be first to scan the documents and then store them as image files. But to re-utilize this information, it would very difficult to read or query text or other information from these image files. Therefore a technique to automatically retrieve and store information, in particular text, from image files is needed. Optical character recognition is an active research area that attempts to develop a computer system with the ability to extract and process text from images automatically. The objective of OCR is to achieve modification or conversion of any form of text or text-containing documents such as handwritten text, printed or scanned text images, into an editable digital format for deeper and further processing. Therefore, OCR enables a machine to automatically recognize text in such documents. Some major challenges need to be recognized and handled in order to achieve a successful automation. The font characteristics of the characters in paper documents and quality of images are only some of the recent challenges. Due to these challenges, characters sometimes may not be recognized correctly by computer system. In this paper we investigate OCR in four different ways. First we give a detailed overview of the challenges that might emerge in OCR stages. Second, we review the general phases of an OCR system such as pre-processing, segmentation, normalization, feature extraction, classification and post-processing. Then, we highlight developments and main applications and uses of OCR and finally, a brief OCR history are discussed. Therefore, this discussion provides a very comprehensive review of the state-of-the-art of the field.

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References

Patel C, Patel A, Patel D. Optical character recognition by open source OCR tool tesseract: A case study. International Journal of Computer Applications. 2012 Jan 1;55(10).

Ye Q, Doermann D. Text detection and recognition in imagery: A survey. IEEE transactions on pattern analysis and machine intelligence. 2015 Jul 1;37(7):1480-500.

Jain A, Dubey A, Gupta R, Jain N, Tripathi P. Fundamental challenges to mobile based ocr. vol. 2013 May;2:86-101.

Moravec K. A Grayscale Reader for Camera Images of Xerox DataGlyphs. InBMVC 2002 Sep (pp. 1-10).

Smith R, Antonova D, Lee DS. Adapting the Tesseract open source OCR engine for multilingual OCR. In Proceedings of the International Workshop on Multilingual OCR 2009 Jul 25 (p. 1). ACM.

Ulges A, Lampert CH, Breuel TM. Document image dewarping using robust estimation of curled text lines. In Eighth International Conference on Document Analysis and Recognition (ICDAR'05) 2005 Aug 31 (pp. 1001-1005). IEEE.

Kaur S, Mann PS, Khurana S. Page Segmentation in OCR System-A Review.

Saha S, Basu S, Nasipuri M, Basu DK. A Hough transform based technique for text segmentation. arXiv preprint arXiv:1002.4048. 2010 Feb 22.

Basu S, Chaudhuri C, Kundu M, Nasipuri M, Basu DK. Text line extraction from multi-skewed handwritten documents. Pattern Recognition. 2007 Jun 30;40(6):1825- 39.

Khandelwal A, Choudhury P, Sarkar R, Basu S, Nasipuri M, Das N. Text line segmentation for unconstrained handwritten document images using neighborhood connected component analysis. InInternational Conference on Pattern Recognition and Machine Intelligence 2009 Dec 16 (pp. 369-374). Springer Berlin Heidelberg.

Shinde AA, Chougule DG. Text Pre-processing and Text Segmentation for OCR. International Journal of Computer Science Engineering and Technology. 2012:810-2.

Trier ØD, Jain AK, Taxt T. Feature extraction methods for character recognition-a survey. Pattern recognition. 1996 Apr 30;29(4):641-62.

Pradeep J, Srinivasan E, Himavathi S. Diagonal based feature extraction for handwritten character recognition system using neural network. InElectronics Computer Technology (ICECT), 2011 3rd International Conference on 2011 Apr 8 (Vol. 4, pp. 364-368). IEEE.

Bishnu A, Bhattacharya BB, Kundu MK, Murthy CA, Acharya T. A pipeline architecture for computing the Euler number of a binary image. Journal of Systems Architecture. 2005 Aug 31;51(8):470-87.

Dinesh Acharya U, Subbareddy NV. Krishnamoorthy: Isolated Kannada Numeral Recognition Using Structural Features and K-Means Cluster. Proc. of IISN. 2007:125-9.

Sharma OP, Ghose MK, Shah KB. An improved zone based hybrid feature extraction model for handwritten alphabets recognition using euler number. International Journal of Soft Computing and Engineering. 2012 May;2(2):504-8.

Suen CY. Character recognition by computer and applications. Handbook of pattern recognition and image processing. 1986:569-86.

Rehman A, Saba T. Neural networks for document image preprocessing: state of the art. Artificial Intelligence Review. 2014 Aug 1;42(2):253-73.

Dongre VJ, Mankar VH. A review of research on Devnagari character recognition. arXiv preprint arXiv:1101.2491. 2011 Jan 13.

Shah P, Karamchandani S, Nadkar T, Gulechha N, Koli K, Lad K. OCR-based chassis-number recognition using artificial neural networks. InVehicular Electronics and Safety (ICVES), 2009 IEEE International Conference on 2009 Nov 11 (pp. 31-34). IEEE.

Zhai X, Bensaali F, Sotudeh R. OCR-based neural network for ANPR. In2012 IEEE International Conference on Imaging Systems and Techniques Proceedings 2012 Jul 16 (pp. 393-397). IEEE.

Shamsher I, Ahmad Z, Orakzai JK, Adnan A. OCR for printed urdu script using feed forward neural network. InProceedings of World Academy of Science, Engineering and Technology 2007 Aug (Vol. 23, pp. 172-175).

Yetirajam M, Nayak MR, Chattopadhyay S. Recognition and classification of broken characters using feed forward neural network to enhance an OCR solution. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume. 2012 Oct 28;1.

Verma R, Ali DJ. A-Survey of Feature Extraction and Classification Techniques in OCR Systems. International Journal of Computer Applications & Information Technology. 2012 Nov;1(3).

Jain AK, Duin RP, Mao J. Statistical pattern recognition: A review. IEEE Transactions on pattern analysis and machine intelligence. 2000 Jan;22(1):4-37.

Matei O, Pop PC, Vălean H. Optical character recognition in real environments using neural networks and k-nearest neighbor. Applied intelligence. 2013 Dec 1;39(4):739-48.

Ganis MD, Wilson CL, Blue JL. Neural network-based systems for handprint OCR applications. IEEE Transactions on Image Processing. 1998 Aug;7(8):1097-112.

Gossweiler R, Kamvar M, Baluja S. What's up CAPTCHA?: a CAPTCHA based on image orientation. In Proceedings of the 18th international conference on World wide web 2009 Apr 20 (pp. 841-850). ACM.

Gao J, Blasch E, Pham K, Chen G, Shen D, Wang Z. Automatic vehicle license plate recognition with color component texture detection and template matching. In SPIE Defense, Security, and Sensing 2013 May 21 (pp. 87390Z-87390Z). International Society for Optics and Photonics.

Mishra N, Patvardhan C. ATMA: Android Travel Mate Application. International Journal of Computer Applications. 2012 Jan 1;50(16).

Mantas J. An overview of character recognition methodologies. Pattern recognition. 1986 Dec 31;19(6):425-30.

Gustav Tauschek. Reading machine. U.S. Patent 2026329, http://www.google.com/patents?vid=USPAT2026329, December 1935FLEXChip Signal Processor (MC68175/D), Motorola, 1996. [Accessed 23/11/2016]

Paul W. Handel. Stat IST ical Machine. U.S. Patent 1915993,http://www.google.com/patents?vid=USPAT1915 993, June 1993. [Accessed 23/11/2016]

Mori S, Suen CY, Yamamoto K. Historical review of OCR research and development. Proceedings of the IEEE. 1992 Jul;80(7):1029-58.

Amin A. Off-line Arabic character recognition: the state of the art. Pattern recognition. 1998 Mar 1;31(5):517-30.

Stallings W. Approaches to Chinese character recognition. Pattern recognition. 1976 Apr 30;8(2):87-98.

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Published

01-12-2016

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Section

Research Articles

How to Cite

[1]
“A Detailed Analysis of Optical Character Recognition Technology”, J. Appl. Methods Electron. Comput., pp. 244–249, Dec. 2016, doi: 10.18100/ijamec.270374.