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Binarization of photographed documents image quality, processing time and size assessment

Published: 18 November 2022 Publication History

Abstract

Today, over eighty percent of the world's population owns a smart-phone with an in-built camera, and they are very often used to photograph documents. Document binarization is a key process in many document processing platforms. This competition on binarizing photographed documents assessed the quality, time, space, and performance of five new algorithms and sixty-four "classical" and alternative algorithms. The evaluation dataset is composed of offset, laser, and deskjet printed documents, photographed using six widely-used mobile devices with the strobe flash on and off, under two different angles and places of capture.

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Cited By

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  • (2024)DocXclassifier: towards a robust and interpretable deep neural network for document image classificationInternational Journal on Document Analysis and Recognition (IJDAR)10.1007/s10032-024-00483-w27:3(447-473)Online publication date: 25-Jun-2024
  • (2023)A Quality, Size and Time Assessment of the Binarization of Documents Photographed by SmartphonesJournal of Imaging10.3390/jimaging90200419:2(41)Online publication date: 13-Feb-2023
  • (2023)A Hybrid Method for Objective Quality Assessment of Binary ImagesIEEE Access10.1109/ACCESS.2023.328916811(63388-63397)Online publication date: 2023

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cover image ACM Conferences
DocEng '22: Proceedings of the 22nd ACM Symposium on Document Engineering
September 2022
118 pages
ISBN:9781450395441
DOI:10.1145/3558100
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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Published: 18 November 2022

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Author Tags

  1. binarization
  2. binarization algorithms
  3. photographed documents
  4. quality evaluation
  5. space evaluation
  6. time evaluation

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DocEng '22
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DocEng '22: ACM Symposium on Document Engineering 2022
September 20 - 23, 2022
California, San Jose

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Cited By

View all
  • (2024)DocXclassifier: towards a robust and interpretable deep neural network for document image classificationInternational Journal on Document Analysis and Recognition (IJDAR)10.1007/s10032-024-00483-w27:3(447-473)Online publication date: 25-Jun-2024
  • (2023)A Quality, Size and Time Assessment of the Binarization of Documents Photographed by SmartphonesJournal of Imaging10.3390/jimaging90200419:2(41)Online publication date: 13-Feb-2023
  • (2023)A Hybrid Method for Objective Quality Assessment of Binary ImagesIEEE Access10.1109/ACCESS.2023.328916811(63388-63397)Online publication date: 2023

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