research-article

Detecting and recognizing tables in spreadsheets

Authors:

Iyad Abu Doush,

Enrico PontelliAuthors Info & Claims

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

Pages 471 - 478

https://doi.org/10.1145/1815330.1815391

Published: 09 June 2010 Publication History

Abstract

Detecting tables in a spreadsheet is the first step needed to make spreadsheet documents accessible to individuals with visual disabilities. Techniques to enable aural presentation and navigation of tables have been proposed, but they assume a thorough knowledge of the structure of the table; on the other hand, boundaries and structure of tables in a spreadsheet are not evident without a visual exploration. This paper presents an algorithm for table recognition in spreadsheets. The algorithm uses three types of cells as its basis: title cell, header cell, and data cell. Different attributes of the cells are used to identify the cell type within a spreadsheet. Hierarchical clustering is used to aggregate cells to compose the functional components of a table. The algorithm has been evaluated on a diverse set of benchmarks with very encouraging results.

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Cited By

Jiang CFan YXie JKuang EZhang KFan M(2024)Designing Unobtrusive Modulated Electrotactile Feedback on Fingertip Edge to Assist Blind and Low Vision (BLV) People in Comprehending ChartsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642546(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642546
Shigarov A(2022)Table understanding: Problem overviewWIREs Data Mining and Knowledge Discovery10.1002/widm.148213:1Online publication date: 21-Nov-2022
https://doi.org/10.1002/widm.1482
Jain PGoel TVerma IShakir MDey LSharma G(2021)Extracting Tabular data for Question-Answering from DocumentsProceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD)10.1145/3430984.3430992(400-404)Online publication date: 2-Jan-2021
https://dl.acm.org/doi/10.1145/3430984.3430992
Show More Cited By

Index Terms

Detecting and recognizing tables in spreadsheets
1. Social and professional topics
  1. Professional topics
    1. Computing profession
      1. Assistive technologies
  2. User characteristics
    1. People with disabilities

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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.

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Association for Computing Machinery

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Publication History

Published: 09 June 2010

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DAS '10

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

June 9 - 11, 2010

Massachusetts, Boston, USA

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Cited By

Jiang CFan YXie JKuang EZhang KFan M(2024)Designing Unobtrusive Modulated Electrotactile Feedback on Fingertip Edge to Assist Blind and Low Vision (BLV) People in Comprehending ChartsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642546(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642546
Shigarov A(2022)Table understanding: Problem overviewWIREs Data Mining and Knowledge Discovery10.1002/widm.148213:1Online publication date: 21-Nov-2022
https://doi.org/10.1002/widm.1482
Jain PGoel TVerma IShakir MDey LSharma G(2021)Extracting Tabular data for Question-Answering from DocumentsProceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD)10.1145/3430984.3430992(400-404)Online publication date: 2-Jan-2021
https://dl.acm.org/doi/10.1145/3430984.3430992
Qiao LLi ZCheng ZZhang PPu SNiu YRen WTan WWu F(2021)LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask AlignmentDocument Analysis and Recognition – ICDAR 202110.1007/978-3-030-86549-8_7(99-114)Online publication date: 2-Sep-2021
https://doi.org/10.1007/978-3-030-86549-8_7
Coetsee DKroon SHoffmann MDe Raedt L(2021)SpLyCI: Integrating Spreadsheets by Recognising and Solving Layout ConstraintsAdvances in Intelligent Data Analysis XIX10.1007/978-3-030-74251-5_32(402-413)Online publication date: 13-Apr-2021
https://doi.org/10.1007/978-3-030-74251-5_32
Escalona Y(2020)Algoritmos para el reconocimiento de estructuras de tablasIngenius10.17163/ings.n25.2021.05(50-61)Online publication date: 31-Dec-2020
https://doi.org/10.17163/ings.n25.2021.05
Paramonov VShigarov AVetrova V(2020)Table Header Correction Algorithm Based on Heuristics for Improving Spreadsheet Data ExtractionInformation and Software Technologies10.1007/978-3-030-59506-7_13(147-158)Online publication date: 8-Oct-2020
https://doi.org/10.1007/978-3-030-59506-7_13
Qasim SMahmood HShafait F(2019)Rethinking Table Recognition using Graph Neural Networks2019 International Conference on Document Analysis and Recognition (ICDAR)10.1109/ICDAR.2019.00031(142-147)Online publication date: Sep-2019
https://doi.org/10.1109/ICDAR.2019.00031
Bernardo IBorges MBaranauskas MSantanchè A(2015)Interpretation of Construction Patterns for Biodiversity SpreadsheetsEnterprise Information Systems10.1007/978-3-319-22348-3_22(397-414)Online publication date: 31-Jul-2015
https://doi.org/10.1007/978-3-319-22348-3_22
Capovilla DHubwieser PCarter JUtting IClear A(2013)Teaching spreadsheets to visually-impaired students in an environment similar to a mainstream classProceedings of the 18th ACM conference on Innovation and technology in computer science education10.1145/2462476.2462477(99-104)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1145/2462476.2462477
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