Article

Using visual cues for extraction of tabular data from arbitrary HTML documents

Authors:

Bernhard Krüpl,

Marcus Herzog,

Wolfgang GatterbauerAuthors Info & Claims

WWW '05: Special interest tracks and posters of the 14th international conference on World Wide Web

Pages 1000 - 1001

https://doi.org/10.1145/1062745.1062838

Published: 10 May 2005 Publication History

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Abstract

We describe a method to extract tabular data from web pages. Rather than just analyzing the DOM tree, we also exploit visual cues in the rendered version of the document to extract data from tables which are not explicitly marked with an HTML table element. To detect tables, we rely on a variant of the well-known X-Y cut algorithm as used in the OCR community. We implemented the system by directly accessing Mozilla's box model that contains the positional data for all HTML elements of a given web page.

References

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Ha Jaekyu, R.M. Haralick, and I.T. Phillips. Recursive x-y cut using bounding boxes of connected components. In Proc. of the 3rd Int. Conf. on Document Analysis and Recognition, pages 952--955, 1995.

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Desai JBohara M(2021)Farmer Connect”- A Step Towards Enabling Machine Learning based Agriculture 4.0 Efficiently2021 6th International Conference on Communication and Electronics Systems (ICCES)10.1109/ICCES51350.2021.9489039(1440-1445)Online publication date: 8-Jul-2021
https://doi.org/10.1109/ICCES51350.2021.9489039
Kim JHwang H(2020)A Rule-Based Method for Table Detection in Website ImagesIEEE Access10.1109/ACCESS.2020.29909018(81022-81033)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2990901
Kravchenko AFayzrakhmanov RSallinger E(2018)Web Page Representations and Data Extraction with BERyLCurrent Trends in Web Engineering10.1007/978-3-030-03056-8_3(22-30)Online publication date: 29-Nov-2018
https://doi.org/10.1007/978-3-030-03056-8_3
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Index Terms

Using visual cues for extraction of tabular data from arbitrary HTML documents
1. Applied computing
  1. Document management and text processing
    1. Document capture
2. Information systems
  1. Information retrieval
  2. Information storage systems

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Published In

WWW '05: Special interest tracks and posters of the 14th international conference on World Wide Web

May 2005

454 pages

ISBN:1595930515

DOI:10.1145/1062745

Conference Chairs:
Allan Ellis
Southern Cross University, Australia
,
Tatsuya Hagino
Keio University, Japan
,
Program Chairs:
Fred Douglis
IBM Research
,
Prabhakar Raghavan
Verity, Inc.

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]

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Association for Computing Machinery

New York, NY, United States

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Published: 10 May 2005

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Desai JBohara M(2021)Farmer Connect”- A Step Towards Enabling Machine Learning based Agriculture 4.0 Efficiently2021 6th International Conference on Communication and Electronics Systems (ICCES)10.1109/ICCES51350.2021.9489039(1440-1445)Online publication date: 8-Jul-2021
https://doi.org/10.1109/ICCES51350.2021.9489039
Kim JHwang H(2020)A Rule-Based Method for Table Detection in Website ImagesIEEE Access10.1109/ACCESS.2020.29909018(81022-81033)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2990901
Kravchenko AFayzrakhmanov RSallinger E(2018)Web Page Representations and Data Extraction with BERyLCurrent Trends in Web Engineering10.1007/978-3-030-03056-8_3(22-30)Online publication date: 29-Nov-2018
https://doi.org/10.1007/978-3-030-03056-8_3
Raza MGulwani SSingh SMarkovitch S(2017)Automated data extraction using predictive program synthesisProceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298239.3298368(882-890)Online publication date: 4-Feb-2017
https://dl.acm.org/doi/10.5555/3298239.3298368
Azir MAhmad K(2017)Wrapper approaches for web data extraction : A review2017 6th International Conference on Electrical Engineering and Informatics (ICEEI)10.1109/ICEEI.2017.8312458(1-6)Online publication date: Nov-2017
https://doi.org/10.1109/ICEEI.2017.8312458
Purnamasari DWicaksana IHarmanto SBanowosari L(2015)HTML table wrapper based on table componentsInternational Journal of Computer Applications in Technology10.1504/IJCAT.2015.07358952:4(237-243)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1504/IJCAT.2015.073589
Polozov OGulwani SMacskassy SPerlich CLeskovec JWang WGhani R(2014)LaSEWebProceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2623330.2623761(741-750)Online publication date: 24-Aug-2014
https://dl.acm.org/doi/10.1145/2623330.2623761
Hu JLiu Y(2014)Analysis of Documents Born DigitalHandbook of Document Image Processing and Recognition10.1007/978-0-85729-859-1_26(775-804)Online publication date: 30-Apr-2014
https://doi.org/10.1007/978-0-85729-859-1_26
Herzog CKordomatis IHolzinger WFayzrakhmanov RKrüpl-Sypien BShin SMaldonado J(2013)Feature-based object identification for web automationProceedings of the 28th Annual ACM Symposium on Applied Computing10.1145/2480362.2480504(742-749)Online publication date: 18-Mar-2013
https://dl.acm.org/doi/10.1145/2480362.2480504
Seth SNagy G(2013)Segmenting Tables via Indexing of Value Cells by Table HeadersProceedings of the 2013 12th International Conference on Document Analysis and Recognition10.1109/ICDAR.2013.181(887-891)Online publication date: 25-Aug-2013
https://dl.acm.org/doi/10.1109/ICDAR.2013.181
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DOM-based content extraction of HTML documents

Robust Web Data Extraction Based on Unsupervised Visual Validation

Visually guided bottom-up table detection and segmentation in web documents

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