An impact of linguistic features on automated classification of OCR texts

Gudila Paul Moshi; Lazaro S.P. Busagala; Wataru Ohyama; Tetsushi Wakabayashi; Fumitaka Kimura

doi:10.1145/1815330.1815367

An impact of linguistic features on automated classification of OCR texts

Gudila Paul Moshi, Lazaro S.P. Busagala, Wataru Ohyama, Tetsushi Wakabayashi, Fumitaka Kimura

Real world robotics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, DAS '10
Pages	287-292
Number of pages	6
DOIs	https://doi.org/10.1145/1815330.1815367
Publication status	Published - 2010
Event	2010 IAPR Workshop on Document Analysis Systems, DAS 2010 - Boston, MA, United States Duration: Jun 9 2010 → Jun 11 2010

Publication series

Name	ACM International Conference Proceeding Series

Other

Other	2010 IAPR Workshop on Document Analysis Systems, DAS 2010
Country/Territory	United States
City	Boston, MA
Period	6/9/10 → 6/11/10

All Science Journal Classification (ASJC) codes

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

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10.1145/1815330.1815367

Cite this

An impact of linguistic features on automated classification of OCR texts. / Moshi, Gudila Paul; Busagala, Lazaro S.P.; Ohyama, Wataru et al.
Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, DAS '10. 2010. p. 287-292 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Moshi, GP, Busagala, LSP, Ohyama, W, Wakabayashi, T & Kimura, F 2010, An impact of linguistic features on automated classification of OCR texts. in Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, DAS '10. ACM International Conference Proceeding Series, pp. 287-292, 2010 IAPR Workshop on Document Analysis Systems, DAS 2010, Boston, MA, United States, 6/9/10. https://doi.org/10.1145/1815330.1815367

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AU - Kimura, Fumitaka

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AB - 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.

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An impact of linguistic features on automated classification of OCR texts

Abstract

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Other

All Science Journal Classification (ASJC) codes

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