A Review on Optical Character Recognition
Article Sidebar
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Main Article Content
Abstract
Nowadays, using a keyboard for entering data is the most common way but sometime it becomes more time consuming and need lots of energy. So, a technique was invented named Optical Character Recognition abbreviated as OCR that transfigures printed as well as handwritten text into machine encoded text by electronic means. OCR has been a topic for research for more than half a century. It electronically and mechanically converts the scanned images which can be handwritten, typewritten or printed text. In general, to figure out the characters of page, OCR compares each scanned letter pixel by pixel to a known database of fonts and decides onto the closest match.
Article Details
References
[1] G.Vamvakas, B.Gatos, N. Stamatopoulos, and S.J.Perantonis: A Complete Optical Character Recognition Methodology for Historical Documents 2007.
[2] Karez Abdulwahhab Hamad, Mehmet Kaya: A Detailed Analysis of Optical Character Recognition Technology. International Journal of Applied Mathematics, Electronics and Computers Advanced Technology and Science ISSN: 2147-8228.
[3] Combination of Document Image Binarization Techniques 2011.
[4] International Conference on Document Analysis and Recognition 2015.
[5] D-Lib Magazine: How Good Can It Get? Analysing and Improvising of OCR Accuracy in Large Scale Historic Newspaper Digitisation Programs.
[6] Raghuraj Singh1 , C. S. Yadav2 , Prabhat Verma3 , Vibhash Yadav4: Optical Character Recognition (OCR) for Printed Devnagari Script Using Artificial Neural Network
[7] B. B. Chaudhary and U. Pal: OCR Error Detection and Correction of an Inflectional Indian Language Script, Pattern Recognition, IEEE Proceeding of 13th International Conference on Image Processing 2002.
[8] Agia Paraskevi, Athens: Institute of Informatics and Telecommunications, National Center for Scientific Research: Demokritos, GR-153 10.
[9] Optical Character Recognition System Using BP Algorithm: Department of Industrial Systems and Information Engineering, Korea University, Sungbuk-gu Anam-dong 5 Ga 1, Seoul 136-701, South Korea.
[10] Dholakia, K., A Survey on Handwritten Character Recognition Techniques for various Indian
[11] Languages, International Journal of Computer Applications, 115(1), pp 17–21, 2015.
[12] Yu, F. T. S., Jutamulia, S. (Editors): Optical Pattern Recognition, Cambridge University Press, 1998.
[13] Mantas, J.: An Overview of Character Recognition Methodologies, Pattern Recognition, 19(6), pp 425–430, 1986.
[14] 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.
[15] 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.
[16] 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).
[17] Md. Anwar Hossain, Optical Character Recognition based on Template Matching(2018)
[18] C. Vasantha Lakshmi1 and C. Patvardhan “An optical character recognition system for printed
[19] Telugu text , Pattern Analysis & Applications”, Category, Theoretical Advances, Volume 7, Number 2 / July, 2004 Pages 190-204