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    <titleInfo>
        <title>Table Structure Recognition Based on Cell Relationship, a Bottom-Up Approach</title>
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    <name type="personal">
        <namePart type="given">Darshan</namePart>
        <namePart type="family">Adiga</namePart>
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    <name type="personal">
        <namePart type="given">Shabir</namePart>
        <namePart type="given">Ahmad</namePart>
        <namePart type="family">Bhat</namePart>
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    <name type="personal">
        <namePart type="given">Muzaffar</namePart>
        <namePart type="given">Bashir</namePart>
        <namePart type="family">Shah</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Viveka</namePart>
        <namePart type="family">Vyeth</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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        <dateIssued>2019-09</dateIssued>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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            <publisher>INCOMA Ltd.</publisher>
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                <placeTerm type="text">Varna, Bulgaria</placeTerm>
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    <abstract>In this paper, we present a relationship extraction based methodology for table structure recognition in PDF documents. The proposed deep learning-based method takes a bottom-up approach to table recognition in PDF documents. We outline the shortcomings of conventional approaches based on heuristics and machine learning-based top-down approaches. In this work, we explain how the task of table structure recognition can be modeled as a cell relationship extraction task and the importance of the bottom-up approach in recognizing the table cells. We use Multilayer Feedforward Neural Network for table structure recognition and compare the results of three feature sets. To gauge the performance of the proposed method, we prepared a training dataset using 250 tables in PDF documents, carefully selecting the table structures that are most commonly found in the documents. Our model achieves an overall accuracy of 97.95% and an F1-Score of 92.62% on the test dataset.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_001</identifier>
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        <url>https://aclanthology.org/R19-1001</url>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T Table Structure Recognition Based on Cell Relationship, a Bottom-Up Approach
%A Adiga, Darshan
%A Bhat, Shabir Ahmad
%A Shah, Muzaffar Bashir
%A Vyeth, Viveka
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F adiga-etal-2019-table
%X In this paper, we present a relationship extraction based methodology for table structure recognition in PDF documents. The proposed deep learning-based method takes a bottom-up approach to table recognition in PDF documents. We outline the shortcomings of conventional approaches based on heuristics and machine learning-based top-down approaches. In this work, we explain how the task of table structure recognition can be modeled as a cell relationship extraction task and the importance of the bottom-up approach in recognizing the table cells. We use Multilayer Feedforward Neural Network for table structure recognition and compare the results of three feature sets. To gauge the performance of the proposed method, we prepared a training dataset using 250 tables in PDF documents, carefully selecting the table structures that are most commonly found in the documents. Our model achieves an overall accuracy of 97.95% and an F1-Score of 92.62% on the test dataset.
%R 10.26615/978-954-452-056-4_001
%U https://aclanthology.org/R19-1001
%U https://doi.org/10.26615/978-954-452-056-4_001
%P 1-8

Markdown (Informal)

[Table Structure Recognition Based on Cell Relationship, a Bottom-Up Approach](https://aclanthology.org/R19-1001) (Adiga et al., RANLP 2019)

• Table Structure Recognition Based on Cell Relationship, a Bottom-Up Approach (Adiga et al., RANLP 2019)

ACL

• Darshan Adiga, Shabir Ahmad Bhat, Muzaffar Bashir Shah, and Viveka Vyeth. 2019. Table Structure Recognition Based on Cell Relationship, a Bottom-Up Approach. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1–8, Varna, Bulgaria. INCOMA Ltd..