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Efficient document image binarization using heterogeneous computing and parameter tuning
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-2161-7371
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0001-9947-1088
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-0535-1761
2018 (English)In: International Journal on Document Analysis and Recognition, ISSN 1433-2833, E-ISSN 1433-2825, Vol. 21, no 1-2, p. 41-58Article in journal (Refereed) Published
Abstract [en]

In the context of historical document analysis, image binarization is a first important step, which separates foreground from background, despite common image degradations, such as faded ink, stains, or bleed-through. Fast binarization has great significance when analyzing vast archives of document images, since even small inefficiencies can quickly accumulate to years of wasted execution time. Therefore, efficient binarization is especially relevant to companies and government institutions, who want to analyze their large collections of document images. The main challenge with this is to speed up the execution performance without affecting the binarization performance. We modify a state-of-the-art binarization algorithm and achieve on average a 3.5 times faster execution performance by correctly mapping this algorithm to a heterogeneous platform, consisting of a CPU and a GPU. Our proposed parameter tuning algorithm additionally improves the execution time for parameter tuning by a factor of 1.7, compared to previous parameter tuning algorithms. We see that for the chosen algorithm, machine learning-based parameter tuning improves the execution performance more than heterogeneous computing, when comparing absolute execution times. © 2018 The Author(s)

Place, publisher, year, edition, pages
Springer Verlag , 2018. Vol. 21, no 1-2, p. 41-58
Keywords [en]
Automatic parameter tuning, Heterogeneous computing, Historical documents, Image binarization, Bins, History, Image analysis, Learning systems, Document image binarization, Government institutions, Heterogeneous platforms, Parameter tuning algorithm, Parameter estimation
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-15891DOI: 10.1007/s10032-017-0293-7ISI: 000433193500003Scopus ID: 2-s2.0-85041228615OAI: oai:DiVA.org:bth-15891DiVA, id: diva2:1182896
Available from: 2018-02-15 Created: 2018-02-15 Last updated: 2018-07-12Bibliographically approved
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Westphal, FlorianGrahn, HåkanLavesson, Niklas

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