An open-source tool for the transcription of paper-spreadsheet data: Code and supplemental materials available online: Https://github.com/deskool/images to spreadsheets

Mohammad M. Ghassemi, Willow Jarvis, Tuka Alhanai, Emery N. Brown, Roger G. Mark, M. Brandon Westover

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Clinical researchers, historians, educators and field researchers alike still regularly capture data on paper spreadsheets. In the case of health care and education, data will often contain sensitive personal information, further complicating the process of transcribing paper-based archives into digital form. In this work, we describe a tool that utilizes machine learning and crowd intelligence to automatically transcribe images of paper-based spreadsheets into electronic form while protecting sensitive personal information. Our solution consists of four high-level stages: (1) the extraction of cell-level images from the spreadsheet grid, (2) machine recognition of digits within the cells, (3) human transcription of cell contents that the machine was uncertain of and (4) feedback of human transcription results to the machine to improve future classification performance. We test the algorithm on a novel data-set of 135 heterogeneous clinical flow-sheet images collected from the Massachusetts General Hospital (MGH), 2 hand-drawn spreadsheets, one chalk-board drawing, and one printed table. we demonstrate that our algorithm provides a generalized solution for spreadsheet transcription that maintains privacy, is up to 10 times faster and twice as cost effective than existing alternatives. Our work is valuable both as a tool and as a starting point for the development of better algorithms.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages935-941
Number of pages7
ISBN (Electronic)9781538627143
DOIs
StatePublished - Jul 1 2017
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: Dec 11 2017Dec 14 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume2018-January

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
CountryUnited States
CityBoston
Period12/11/1712/14/17

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Keywords

  • Crowd-Sourcing
  • Image Segmentation
  • Optical Character Recognition
  • Software
  • Transcription

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Control and Optimization

Cite this

Ghassemi, M. M., Jarvis, W., Alhanai, T., Brown, E. N., Mark, R. G., & Westover, M. B. (2017). An open-source tool for the transcription of paper-spreadsheet data: Code and supplemental materials available online: Https://github.com/deskool/images to spreadsheets. In J-Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, & M. Toyoda (Eds.), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (pp. 935-941). (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8258012