research-article

An impact of linguistic features on automated classification of OCR texts

Authors:

Gudila Paul Moshi,

Lazaro S. P. Busagala,

Tetsushi Wakabayashi,

Fumitaka KimuraAuthors Info & Claims

DAS '10: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

Pages 287 - 292

https://doi.org/10.1145/1815330.1815367

Published: 09 June 2010 Publication History

Abstract

Optical Character reader (OCR) systems can be used in digitizing print documents. OCR texts are generated in the process of digitizing print documents. Usually these texts need to be indexed and organized to simplify their access and retrieval. This can be done by the use of automatic classification techniques. However it is currently impossible for OCR technology to recognize all characters with an accuracy of 100%. Furthermore it is not known whether part of speech (POS) analysis contributes to proper OCR texts representation in a discriminative way. Conventionally, the bag-of-words approach is used in OCR text classification. In this paper we experimentally evaluated POS analysis on OCR texts to formulate an informative feature set. Empirical results indicate that the combination of suitably selected POS improved classification performance of OCR texts.

References

[1]

L. S. Busagala, W. Ohyama, T. Wakabayashi, and F. Kimura. Improving automatic text classification by integrated feature analysis. IEICE - Trans. Inf. Syst., E91--D(4):1101--1109, 2008.

Digital Library

[2]

S. Chapman. Measuring search retrieval accuracy of uncorrected ocr: Findings from the harvard-radcliffe online historical reference shelf digitization project. Harvard University Library, Harvard, 2001.

[3]

P. Frasconi, G. Soda, and A. Vullo. Text categorization for multi-page documents: A hybrid naive bayes hmm approach. In In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, pages 11--20. ACM Press, 2001.

Digital Library

[4]

K. Fukunaga. Introduction to Statistical Pattern Recognition. Academic Press, 2 edition, 1990.

Digital Library

[5]

T. Joachims. Learning to classify text using support vector machines: Methods, Theory and Algorithms. Kluwer Academic Publishers Boston Dordrecht London, 2001.

Digital Library

[6]

S. Lam and L. Lee. Feature reduction for neural network based text categorization. In Proceedings of DASFAA-99, 6^th IEEE International Conference on Database Advanced systems for advanced applications, pages 195--202, 1999. Hsinchu, TW.

Digital Library

[7]

H.-S. Lim. Improving kNN based text classification with well estimated parameters. In International Conference on Neural Information Processing, pages 516--523, 2004.

[8]

M. Murata, L. S. P. Busagala, W. Ohyama, T. Wakabayashi, and F. Kimura. The impact of ocr accuracy and feature transformation on automatic text classification. In Document Analysis Systems, pages 506--517, 2006.

Digital Library

[9]

J. Rennie, L. Shih, J. Teevan, and D. Karger. Tackling the poor assumptions of naive bayes text classifiers. In Proceedings of the 12th International Conference on Machine Learning (ICML), pages 616--623, Washington DC, 2003).

[10]

H. Schmid. Probabilistic part-of-speech tagging using decision trees, 1994.

[11]

F. Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1--47, 2002.

Digital Library

[12]

F. Sebastiani and C. N. D. Ricerche. Machine learning in automated text categorization. ACM Computing Surveys, 34:1--47, 2002.

Digital Library

[13]

K. Taghva, T. Nartker, A. Condit, and J. Borsack. Automatic removal of ąÈgarbage stringsąÉ in ocr text: An implementation.

[14]

Y. Yang. An evaluation of statistical approaches to text categorization. Journal of Information Retrieval, 1:67--88, 1999.

Digital Library

[15]

Y. Yang and X. Liu. A re-examination of text categorization methods. In Proceedings of the Twenty-First International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 42--49, 1999.

Digital Library

Cited By

Rusli FAdhiguna KIrawan H(2021)Indonesian ID Card Extractor Using Optical Character Recognition and Natural Language Post-Processing2021 9th International Conference on Information and Communication Technology (ICoICT)10.1109/ICoICT52021.2021.9527510(621-626)Online publication date: 3-Aug-2021
https://doi.org/10.1109/ICoICT52021.2021.9527510

Index Terms

An impact of linguistic features on automated classification of OCR texts
1. Applied computing
  1. Document management and text processing

Recommendations

An Impact of OCR Errors on Automated Classification of OCR Japanese Texts with Parts-of-Speech Analysis
ICDAR '11: Proceedings of the 2011 International Conference on Document Analysis and Recognition

The technology of Optical Character Recognition (OCR) is used to generate texts in the process of digitizing print documents. Usually these texts need to be indexed and organized to simplify their access and retrieval. One of the powerful approaches in ...
Deep Learning Analysis in Development of Handwritten and Plain Text Classification API
ICONETSI '22: Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry

Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) are technologies that enable text recognition. The difference between OCR and HTR is one designed specifically for digital text and one designed for handwritten text. There are ...
Nastalique segmentation-based approach for Urdu OCR

Much work on Arabic language optical character recognition (OCR) has been on Naskh writing style. Nastalique style, used for most of languages using Arabic script across Southern Asia, is much more challenging to process due to its compactness, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

DAS '10: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

June 2010

490 pages

ISBN:9781605587738

DOI:10.1145/1815330

General Chairs:
David Doermann
University of Maryland, College Park
,
Venu Govindaraju
University at Buffalo, SUNY
,
Daniel Lopresti
Lehigh University
,
Prem Natarajan
Raytheon BBN Technologies

Copyright © 2010 ACM.

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 June 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

DAS '10

DAS '10: The Eighth IAPR International Workshop on Document Analysis Systems

June 9 - 11, 2010

Massachusetts, Boston, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
201
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Rusli FAdhiguna KIrawan H(2021)Indonesian ID Card Extractor Using Optical Character Recognition and Natural Language Post-Processing2021 9th International Conference on Information and Communication Technology (ICoICT)10.1109/ICoICT52021.2021.9527510(621-626)Online publication date: 3-Aug-2021
https://doi.org/10.1109/ICoICT52021.2021.9527510

View Options

Get Access

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.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents