Competition on Binarizing Photographed Document Images 2024 Quality, Time and Space Report

Many document processing platforms have image binarization as a key step. The performance of binarization algorithms depends on several factors that span from the quality of the digitalization devices to the intrinsic features of the document itself and the kind and intensity of the noises present in the image. Besides that, the processing time is also an important factor that may restrict the applicability of a given algorithm. This competition on binarizing photographed documents assessed the quality, time, space, and performance of twenty three new algorithms and seventy-five "classical" algorithms. The test dataset is composed of offset and deskjet printed documents, photographed using six widely-used mobile devices with the strobe flash on, off, and in auto modes under two different angles and places of capture.

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Published
2024-08-20
DOI
https://doi.org/10.1145/3685650.3686793
Citations
1
Primary Topic
Handwritten Text Recognition Techniques
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article
Field-Weighted Citation Impact
0.25
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article

Competition on Binarizing Photographed Document Images 2024 Quality, Time and Space Report

Ricardo da Silva Barboza, Gustavo P. Chaves, Steve Simske, Rafael Dueire Lins et al.
1 citations
Handwritten Text Recognition Techniques
0.25
article

Competition on Binarizing Photographed Document Images 2024 Quality, Time and Space Report

Ricardo da Silva Barboza, Gustavo P. Chaves, Steve Simske, Rafael Dueire Lins, Gabriel de F. P e Silva, Thaylor Vieira
article en
1 citations

Abstract

Many document processing platforms have image binarization as a key step. The performance of binarization algorithms depends on several factors that span from the quality of the digitalization devices to the intrinsic features of the document itself and the kind and intensity of the noises present in the image. Besides that, the processing time is also an important factor that may restrict the applicability of a given algorithm. This competition on binarizing photographed documents assessed the quality, time, space, and performance of twenty three new algorithms and seventy-five "classical" algorithms. The test dataset is composed of offset and deskjet printed documents, photographed using six widely-used mobile devices with the strobe flash on, off, and in auto modes under two different angles and places of capture.

Hospital das Clínicas da Universidade Federal de Pernambuco (BR), Universidade do Estado do Amazonas (BR), Universidade Federal Rural de Pernambuco (BR), Colorado State University (US)
Climate action
Openalex Percentile: Top 47%
Handwritten Text Recognition Techniques
0.25
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