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Optical Character Recognition on Handheld Devices

by Sravan Ch, Shivanku Mahna, Nirbhay Kashyap
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 22
Year of Publication: 2015
Authors: Sravan Ch, Shivanku Mahna, Nirbhay Kashyap
10.5120/20281-2833

Sravan Ch, Shivanku Mahna, Nirbhay Kashyap . Optical Character Recognition on Handheld Devices. International Journal of Computer Applications. 115, 22 ( April 2015), 10-13. DOI=10.5120/20281-2833

@article{ 10.5120/20281-2833,
author = { Sravan Ch, Shivanku Mahna, Nirbhay Kashyap },
title = { Optical Character Recognition on Handheld Devices },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 22 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 10-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume115/number22/20281-2833/ },
doi = { 10.5120/20281-2833 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:55:33.177475+05:30
%A Sravan Ch
%A Shivanku Mahna
%A Nirbhay Kashyap
%T Optical Character Recognition on Handheld Devices
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 22
%P 10-13
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The paper explains how an Optical Character Recognition system (OCR) works and how this system enables us in capturing an image of a text document. It also explains how OCR is more efficient and easier alternative to scanning a document using a scanner as the image captured using OCR is of exactly the same quality like its scanned copy, the only difference being that OCR is done with the help of a simple mobile phone camera whereas scanning is done using a bulky scanner. It then also explains the problems being faced by the developers in using OCR as a technology on a large scale and how that problem can be dealt with. The proposed OCR system provides many features that require no typing, editing raw data, quick translation, and memory utilization. In the end it also highlights the major emerging trends in the field of OCR and how OCR as a technology is evolving with every passing day.

References
  1. Heuristic-Based OCR Post-Correction for Smart Phone Applications, the University of North Carolina at Chapel Hill department of computer science honors thesis Author: Wing-Soon Wilson Lian 2009.
  2. R. W. Smith, The Extraction and Recognition of Text from Multimedia Document Images, PhD Thesis, University of Bristol, November 1987.
  3. The Tesseract open source OCR engine, http://code. google. com/p/tesseract-ocr.
  4. R. Smith. "An overview of the Tesseract OCR Engine. " Proc 9th Int. Conf. on Document Analysis and Recognition, IEEE, Curitiba, Brazil,Sep 2007
  5. "?-Soft: An English Language OCR", 2010 Second InternationalConference on Computer Engineering and Applications. Junaid Tariq,Umar Nauman Muhammad UmairNaru.
  6. A survey of modern optical character recognition techniques (DRAFT), February 2004
Index Terms

Computer Science
Information Sciences

Keywords

Optical Character Recognition System (OCR) Camera Captured Document Images Handheld Device Image Segmentation

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