research-article

Faithful mathematical formula recognition from PDF documents

Authors:

Josef B. Baker,

Alan P. Sexton,

Volker SorgeAuthors Info & Claims

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

Pages 485 - 492

https://doi.org/10.1145/1815330.1815393

Published: 09 June 2010 Publication History

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Abstract

We present an approach to extracting mathematical formulae directly from PDF documents. We exploit both the perfect character information as well as additional font and spacing information available from a PDF document to ensure a faithful recognition of mathematical expressions. The extracted information can be post-processed to produce suitable markup that can be re-inserted into the PDF documents in order to enable the handling of mathematical formulae by accessibility technology. Furthermore, we demonstrate how we recognise different types of mathematical objects, such as relations, operators, etc., without reference to predefined knowledge or dictionary lookup, using character clustering and interspace and character font information alone, all of which contributes to our goal of reconstructing the intended semantics of a formula from its presentation.

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Amador BLangsenkamp MDey AShah AZanibbi RChen HDuh WHuang HKato MMothe JPoblete B(2023)Searching the ACL Anthology with Math Formulas and TextProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591803(3110-3114)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591803
Sakshi Kukreja V(2023)Machine learning and non-machine learning methods in mathematical recognition systems: Two decades’ systematic literature reviewMultimedia Tools and Applications10.1007/s11042-023-16356-z83:9(27831-27900)Online publication date: 23-Aug-2023
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Published In

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

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

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Published: 09 June 2010

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

June 9 - 11, 2010

Massachusetts, Boston, USA

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Amador BLangsenkamp MDey AShah AZanibbi RChen HDuh WHuang HKato MMothe JPoblete B(2023)Searching the ACL Anthology with Math Formulas and TextProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591803(3110-3114)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591803
Sakshi Kukreja V(2023)Machine learning and non-machine learning methods in mathematical recognition systems: Two decades’ systematic literature reviewMultimedia Tools and Applications10.1007/s11042-023-16356-z83:9(27831-27900)Online publication date: 23-Aug-2023
https://doi.org/10.1007/s11042-023-16356-z
Kukreja VSakshi (2022)Machine learning models for mathematical symbol recognition: A stem to stern literature analysisMultimedia Tools and Applications10.1007/s11042-022-12644-281:20(28651-28687)Online publication date: 31-Mar-2022
https://doi.org/10.1007/s11042-022-12644-2
Sakshi Kukreja V(2022)A dive in white and grey shades of ML and non-ML literature: a multivocal analysis of mathematical expressionsArtificial Intelligence Review10.1007/s10462-022-10330-156:7(7047-7135)Online publication date: 6-Dec-2022
https://dl.acm.org/doi/10.1007/s10462-022-10330-1
Grijalva FSantos EAcuna BRodriguez JLarco J(2021)Deep Learning in Time-Frequency Domain for Document Layout AnalysisIEEE Access10.1109/ACCESS.2021.31259139(151254-151265)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3125913
Sakshi Kukreja V(2021)A retrospective study on handwritten mathematical symbols and expressions: Classification and recognitionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2021.104292103(104292)Online publication date: Aug-2021
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Wang ZLiu J(2020)PDF2LaTeXProceedings of the ACM Symposium on Document Engineering 202010.1145/3395027.3419580(1-10)Online publication date: 29-Sep-2020
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Wang ZBeyette DLin JLiu J(2019)Extraction of Math Expressions from PDF Documents Based on Unsupervised Modeling of Fonts2019 International Conference on Document Analysis and Recognition (ICDAR)10.1109/ICDAR.2019.00068(381-386)Online publication date: Sep-2019
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Zhang XGao LYuan KLiu RJiang ZTang Z(2017)A Symbol Dominance Based Formulae Recognition Approach for PDF Documents2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)10.1109/ICDAR.2017.189(1144-1149)Online publication date: Nov-2017
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Youssef A(2017)Part-of-Math Tagging and ApplicationsIntelligent Computer Mathematics10.1007/978-3-319-62075-6_25(356-374)Online publication date: 28-Jun-2017
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