A comparison of whitespace normalization methods in a text art extraction method with run length encoding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Text based pictures called text art or ASCII art can be noise in text processing and display of text, though they enrich expression in Web pages, email text and so on. With text art extraction methods, which detect text art areas in a given text data, we can ignore text arts in a given text data or replace them with other strings. We proposed a text art extraction method with Run Length Encoding in our previous work. We, however, have not considered how to deal with whitespaces in text arts. In this paper, we propose three whitespace normalization methods in our text art extraction method, and compare them by an experiment. According to the results of the experiment, the best method in the three is a method which replaces each wide width whitespace with two narrow width whitespaces. It improves the average of F-measure of the precision and the recall by about 4%.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages135-142
Number of pages8
Volume7004 LNAI
EditionPART 3
DOIs
Publication statusPublished - 2011
Event3rd International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011 - Taiyuan
Duration: 2011 Sep 242011 Sep 25

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume7004 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011
CityTaiyuan
Period11/9/2411/9/25

Fingerprint

Text processing
Electronic mail
Websites
Experiments
Display devices

Keywords

  • Information Extraction
  • Natural Language Processing
  • Pattern Recognition

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Suzuki, T. (2011). A comparison of whitespace normalization methods in a text art extraction method with run length encoding. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 ed., Vol. 7004 LNAI, pp. 135-142). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7004 LNAI, No. PART 3). https://doi.org/10.1007/978-3-642-23896-3_16

A comparison of whitespace normalization methods in a text art extraction method with run length encoding. / Suzuki, Tetsuya.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7004 LNAI PART 3. ed. 2011. p. 135-142 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7004 LNAI, No. PART 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Suzuki, T 2011, A comparison of whitespace normalization methods in a text art extraction method with run length encoding. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 edn, vol. 7004 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 7004 LNAI, pp. 135-142, 3rd International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011, Taiyuan, 11/9/24. https://doi.org/10.1007/978-3-642-23896-3_16
Suzuki T. A comparison of whitespace normalization methods in a text art extraction method with run length encoding. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 ed. Vol. 7004 LNAI. 2011. p. 135-142. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-642-23896-3_16
Suzuki, Tetsuya. / A comparison of whitespace normalization methods in a text art extraction method with run length encoding. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7004 LNAI PART 3. ed. 2011. pp. 135-142 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
@inproceedings{608c9f9f4c1c47f2bd6ba6e3f684e677,
title = "A comparison of whitespace normalization methods in a text art extraction method with run length encoding",
abstract = "Text based pictures called text art or ASCII art can be noise in text processing and display of text, though they enrich expression in Web pages, email text and so on. With text art extraction methods, which detect text art areas in a given text data, we can ignore text arts in a given text data or replace them with other strings. We proposed a text art extraction method with Run Length Encoding in our previous work. We, however, have not considered how to deal with whitespaces in text arts. In this paper, we propose three whitespace normalization methods in our text art extraction method, and compare them by an experiment. According to the results of the experiment, the best method in the three is a method which replaces each wide width whitespace with two narrow width whitespaces. It improves the average of F-measure of the precision and the recall by about 4{\%}.",
keywords = "Information Extraction, Natural Language Processing, Pattern Recognition",
author = "Tetsuya Suzuki",
year = "2011",
doi = "10.1007/978-3-642-23896-3_16",
language = "English",
isbn = "9783642238956",
volume = "7004 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 3",
pages = "135--142",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
edition = "PART 3",

}

TY - GEN

T1 - A comparison of whitespace normalization methods in a text art extraction method with run length encoding

AU - Suzuki, Tetsuya

PY - 2011

Y1 - 2011

N2 - Text based pictures called text art or ASCII art can be noise in text processing and display of text, though they enrich expression in Web pages, email text and so on. With text art extraction methods, which detect text art areas in a given text data, we can ignore text arts in a given text data or replace them with other strings. We proposed a text art extraction method with Run Length Encoding in our previous work. We, however, have not considered how to deal with whitespaces in text arts. In this paper, we propose three whitespace normalization methods in our text art extraction method, and compare them by an experiment. According to the results of the experiment, the best method in the three is a method which replaces each wide width whitespace with two narrow width whitespaces. It improves the average of F-measure of the precision and the recall by about 4%.

AB - Text based pictures called text art or ASCII art can be noise in text processing and display of text, though they enrich expression in Web pages, email text and so on. With text art extraction methods, which detect text art areas in a given text data, we can ignore text arts in a given text data or replace them with other strings. We proposed a text art extraction method with Run Length Encoding in our previous work. We, however, have not considered how to deal with whitespaces in text arts. In this paper, we propose three whitespace normalization methods in our text art extraction method, and compare them by an experiment. According to the results of the experiment, the best method in the three is a method which replaces each wide width whitespace with two narrow width whitespaces. It improves the average of F-measure of the precision and the recall by about 4%.

KW - Information Extraction

KW - Natural Language Processing

KW - Pattern Recognition

UR - http://www.scopus.com/inward/record.url?scp=80054079604&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80054079604&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-23896-3_16

DO - 10.1007/978-3-642-23896-3_16

M3 - Conference contribution

SN - 9783642238956

VL - 7004 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 135

EP - 142

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -