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taka at the FinSBD-3 task: Tables and Figures Extraction using Object Detection Techniques

Published: 03 June 2021 Publication History

Abstract

FinSBD-3 is a shared task organized in the context of the 1st workshop on Financial Technology on the Web. The task focuses on extracting the entire structure of noisy PDF financial documents that include 1) sentences, lists, items, and organization of lists and items; 2) figures and tables; 3) headers and footers. This paper describes the approach that allows us to extract the figures and tables using their visual cues. We applied the object segmentation techniques in image processing to detect the location of figures and tables in the PDF files. A post-processing method is then executed in order to find exact content. The result shows the potential of this approach.

References

[1]
FinSim & FinSBD FinWeb. [n.d.]. Shared Task - FinSBD-3: The 3rd Shared Task on Structure Boundary Detection, an extension of Sentence Boundary Detection. Retrieved Feb 20, 2021 from https://sites.google.com/nlg.csie.ntu.edu.tw/finweb2021/shared-task-finsbd-3
[2]
Janvijay Singh. [n.d.]. PublishInCovid19 at the FinSBD-2 Task: Sentence and List Extraction in Noisy PDF Text Using a Hybrid Deep Learning and Rule-Based Approach. Retrieved 2020 from https://www.aclweb.org/anthology/2020.finnlp-1.9
[3]
Ultralytics. [n.d.]. Yolov5. Retrieved Feb 20, 2021 from https://github.com/ultralytics/yolov5
[4]
Shou-tao Xu Zhong-Qiu Zhao and Xindong Wu. [n.d.]. Object Detection with Deep Learning: A Review. Retrieved 2019 from https://arxiv.org/pdf/1807.05511.pdf

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cover image ACM Conferences
WWW '21: Companion Proceedings of the Web Conference 2021
April 2021
726 pages
ISBN:9781450383134
DOI:10.1145/3442442
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

Publication History

Published: 03 June 2021

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Author Tags

  1. FinSBD
  2. NLP
  3. Object Detection
  4. PDF
  5. Tables and Figures Extractions

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  • Research-article
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  • Refereed limited

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WWW '21
Sponsor:
WWW '21: The Web Conference 2021
April 19 - 23, 2021
Ljubljana, Slovenia

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

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