An open-source tool for the transcription of paper-spreadsheet data: Code and supplemental materials available online: Https://github.com/deskool/images to spreadsheets
Name
GhassemiIEEEBigD2017.pdf
Description
Accepted version
Size
7.07 MB
Format
Adobe PDF
Checksum (MD5)
88abdf37b09469fb75e3706e4101036f
Author(s)
Ghassemi, Mohammad Mahdi
Jarvis, Willow
Alhanai, Tuka
Brown, Emery Neal
Mark, Roger G
Westover, M. Brandon
Date Issued
January 2018
Journal
2017 IEEE International Conference on Big Data (Big Data)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Ghassemi, Mohammad Mahdi et al. "An open-source tool for the transcription of paper-spreadsheet data: Code and supplemental materials available online: Https://github.com/deskool/images to spreadsheets." 2017 IEEE International Conference on Big Data (Big Data), December 2017, Boston, Massachusetts, USA, Institute of Electrical and Electronics Engineers (IEEE), January 2018 © 2017 IEEE
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/bigdata.2017.8258012
