research-article

An optical character recognition approach to qualifying thresholding algorithms

Authors:

Margaret Sturgill,

Steven J. SimskeAuthors Info & Claims

DocEng '08: Proceedings of the eighth ACM symposium on Document engineering

Pages 263 - 266

https://doi.org/10.1145/1410140.1410197

Published: 16 September 2008 Publication History

Get Access

Abstract

Pre-processing for raster image based document segmentation begins with image thresholding, which is a binarization process separating foreground from background. In this paper, we compare an existing (Otsu), modified existing (Kittler-Illingworth) and simple peak-based thresholding approach on a set of 982 documents for which existing ground truth (full text) is available. We use the output of an open source OCR engine which incorporates an adaptive/dynamic thresholder that can be bypassed by one of the three global thresholds we tested. This allowed comparison of these three approaches in the aggregate. We then used an independently-generated dictionary as a means of characterizing thresholder efficacy. Such an approach, if successful, will provide the means for selecting an optimal thresholder in the absence of a large set of ground truthed documents. Our preliminary findings here indicate that this approach may provide a reliable means for thresholder comparison and eventually preclude the need for time-intensive human ground truthing.

References

[1]

Otsu, N. 1979 A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. Vol. SMC-9, No. 1, 62--66.

Google Scholar

[2]

Kittler, J. and Illingworth, J. 1986 Minimum error thresholding. Pattern Recognition, Vol. 19, No. 1, 41--47.

Digital Library

Google Scholar

[3]

Mitchell, B.T. and Gillies, A.M. 1989 A model-based computer vision system for recognizing handwritten ZIP codes. Machine Vision and Applications Vol. 2, 231--243.

Crossref

Google Scholar

[4]

http://code.google.com/p/tesseract-ocr/ on 4 June 2008.

Google Scholar

[5]

Rice S., Jenkins F., and Nartker T. 1995 The Fourth Annual Test of OCR Accuracy. Technical Report 95-04, Information Science Research Institute, University of Nevada, Las Vegas

Google Scholar

[6]

Simske, S.J., Wright, D.W. and Sturgill, M. 2006 Metaalgorithmic systems for document classification. Proc. 2006 ACM Symp. Doc. Engr., 98--106.

Digital Library

Google Scholar

[7]

http://www.imagemagick.org/ on June 6, 2008.

Google Scholar

[8]

http://www.isri.unlv.edu/ISRI/OCRtk on June 6, 2008.

Google Scholar

Cited By

View all

Obafemi-Ajayi TAgam G(2012)Character-Based Automated Human Perception Quality Assessment in Document ImagesIEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans10.1109/TSMCA.2011.217041742:3(584-595)Online publication date: 1-May-2012
https://dl.acm.org/doi/10.1109/TSMCA.2011.2170417
Likforman-Sulem LDarbon JSmith E(2009)Pre-Processing of Degraded Printed Documents by Non-local Means and Total Variation2009 10th International Conference on Document Analysis and Recognition10.1109/ICDAR.2009.210(758-762)Online publication date: Jul-2009
https://doi.org/10.1109/ICDAR.2009.210
Pimentel MBulterman DSoares L(2009)Document engineering approaches toward scalable and structured multimedia, web and printable documentsMultimedia Tools and Applications10.1007/s11042-009-0288-643:3(195-202)Online publication date: 1-Jul-2009
https://dl.acm.org/doi/10.1007/s11042-009-0288-6

Index Terms

An optical character recognition approach to qualifying thresholding algorithms
1. Applied computing
  1. Document management and text processing
    1. Document capture
      1. Document scanning
      2. Optical character recognition
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Video segmentation
      2. Image and video acquisition

Recommendations

Character and numeral recognition for non-Indic and Indic scripts: a survey
Abstract
A collection of different scripts is employed in writing languages throughout the world. Character and numeral recognition of a particular script is a key area in the field of pattern recognition. In this paper, we have presented a comprehensive ...
New architectural optical character recognition approach for cursive fonts: the historical Maghrebian font as an example

The historical Maghrebian font is an Arabic font that dominated in several North African lands. Various cultural and scientific papers of major importance were developed using this font. In this paper, the full OCR architecture that is able to treat the ...
An optical character recognition system for printed Telugu text

Telugu is one of the oldest and popular languages of India, spoken by more than 66 million people, especially in South India. Not much work has been reported on the development of optical character recognition (OCR) systems for Telugu text. Therefore, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

DocEng '08: Proceedings of the eighth ACM symposium on Document engineering

September 2008

312 pages

ISBN:9781605580814

DOI:10.1145/1410140

General Chair:
Maria de Graça Pimentel
Universidade de Sño Paulo, Brazil
,
Program Chairs:
Dick C.A. Bulterman
CWI and VU, Netherlands
,
Luis Fernando Gomes Soares
PUC-Rio, Brazil

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SIGDOC: ACM Special Interest Group for Design of Communications

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

DocEng '08

Sponsor:

DocEng '08: ACM Symposium on Document Engineering

September 16 - 19, 2008

Sao Paulo, Brazil

Acceptance Rates

DocEng '08 Paper Acceptance Rate 21 of 62 submissions, 34%;

Overall Acceptance Rate 194 of 564 submissions, 34%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
437
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Obafemi-Ajayi TAgam G(2012)Character-Based Automated Human Perception Quality Assessment in Document ImagesIEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans10.1109/TSMCA.2011.217041742:3(584-595)Online publication date: 1-May-2012
https://dl.acm.org/doi/10.1109/TSMCA.2011.2170417
Likforman-Sulem LDarbon JSmith E(2009)Pre-Processing of Degraded Printed Documents by Non-local Means and Total Variation2009 10th International Conference on Document Analysis and Recognition10.1109/ICDAR.2009.210(758-762)Online publication date: Jul-2009
https://doi.org/10.1109/ICDAR.2009.210
Pimentel MBulterman DSoares L(2009)Document engineering approaches toward scalable and structured multimedia, web and printable documentsMultimedia Tools and Applications10.1007/s11042-009-0288-643:3(195-202)Online publication date: 1-Jul-2009
https://dl.acm.org/doi/10.1007/s11042-009-0288-6

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Character and numeral recognition for non-Indic and Indic scripts: a survey

New architectural optical character recognition approach for cursive fonts: the historical Maghrebian font as an example

An optical character recognition system for printed Telugu text

Comments

Information

Published In

Sponsors

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations