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Character identification using document image analysis

Author Affiliations

  • 1Department of ETE, Bhilai Institute of Technology, Raipur, CSVTU, Bhilai, CG, India
  • 2Department of ETE, Bhilai Institute of Technology, Raipur, CSVTU, Bhilai, CG, India

Res. J. Engineering Sci., Volume 7, Issue (7), Pages 5-8, July,26 (2018)

Abstract

The interdisciplinary field of computer vision combined with the powerful algorithms of machine learning can be used to build intelligent electronic systems for various domains of data analysis. Document layout analysis deals with the identification and categorization of the geometric and logical characteristics of text elements from the scanned documents. An OCR engine is used to convert images of typed or handwritten text into machine code. This research uses a Raspberry Pi3 processor module along with suitable peripherals and the entire system is supported by Python programming.

References

  1. Maini R. and Aggarwal H. (2009)., Study and comparison of various image edge detection techniques., International journal of image processing (IJIP), 3(1), 1-11. ISSN 1985-2304.
  2. Balamurugan E., Sangeetha K. and Sengottuvelan P. (2011)., Document Image Analysis -A Review., International journal of Computer application, 1(1). ISSN-2250-1797.
  3. Aaron James S., Sanjana S. and Monisha M. (2015)., OCR based automatic book reader for the visually impaired using Raspberry PI., International Journal of Innovative Research in Computer and Communication Engineering, 4(7), 1111-1118. ISSN-2320-9801.
  4. Dongre V.J. and Mankar V.H. (2010)., A Review of Research on Devnagari Character Recognition., International Journal of Computer Applications, 12(2), 2. ISSN NO 0975- 8887.
  5. Element 14 Community (2015)., Raspberry pi 3 model GPIO 40 pin block Pinout., https://www.element14.com/ community/docs/DOC-73950/l/raspberry-pi-3-model-b-gpio-40-pin-block-pinout raspberrypi.org/forums 2015.
  6. Senthilkumar G., Gopalakrishnan K. and Kumar V.S. (2014)., Embedded image capturing system using raspberry pi system., International Journal of Emerging Trends & Technology in Computer Science, 3(2), 213-215. ISSN 2278-6856.
  7. Engelsma J.J., Cao K. and Jain A.K. (2017)., Raspi Reader: Open Source Fingerprint Reader., arXiv preprint arXiv:1712.09392.
  8. Gurav M.D., Salimath S.S., Hatti S.B., Byakod V.I. and Kanade S. (2017)., B-LIGHT: A Reading aid for the Blind People using OCR and Open CV., International Journal of Scientific Research Engineering & Technology (IJSRET), 6(5). 546-548. ISSN 2278 0882.
  9. Jabeen F.A., Ramamurthy B. and Latha N.A. (2017)., Development and implementation using Arduino and Raspberry Pi based Ignition control system., Advances in Computational Sciences and Technology, 10(7), 989-2004. ISSN 0973-6107
  10. Bukhari S.S., Shafait F. and Breuel T.M. (2012)., Layout analysis of Arabic script documents., In Guide to OCR for Arabic scripts, Springer, London, 35-53. ISBN978-1-4471-4072-6.
  11. Jones J.D., Witek K., Verweij W., Jupe F., Cooke D., Dorling S. and Foster S. (2014)., Elevating crop disease resistance with cloned genes., Philosophical Transactions of the Royal Society of London B: Biological Sciences, 369(1639), 20130087.