@inproceedings{adiga-etal-2019-table,
    title = "Table Structure Recognition Based on Cell Relationship, a Bottom-Up Approach",
    author = "Adiga, Darshan  and
      Bhat, Shabir Ahmad  and
      Shah, Muzaffar Bashir  and
      Vyeth, Viveka",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://aclanthology.org/R19-1001",
    doi = "10.26615/978-954-452-056-4_001",
    pages = "1--8",
    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.",
}