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Document Image Binarization Using Recurrent Neural Networks
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0002-2161-7371
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0002-0535-1761
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0001-9947-1088
2018 (English)In: Proceedings - 13th IAPR International Workshop on Document Analysis Systems, DAS 2018, 2018, p. 263-268Conference paper, Published paper (Refereed)
Abstract [en]

In the context of document image analysis, image binarization is an important preprocessing step for other document analysis algorithms, but also relevant on its own by improving the readability of images of historical documents. While historical document image binarization is challenging due to common image degradations, such as bleedthrough, faded ink or stains, achieving good binarization performance in a timely manner is a worthwhile goal to facilitate efficient information extraction from historical documents. In this paper, we propose a recurrent neural network based algorithm using Grid Long Short-Term Memory cells for image binarization, as well as a pseudo F-Measure based weighted loss function. We evaluate the binarization and execution performance of our algorithm for different choices of footprint size, scale factor and loss function. Our experiments show a significant trade-off between binarization time and quality for different footprint sizes. However, we see no statistically significant difference when using different scale factors and only limited differences for different loss functions. Lastly, we compare the binarization performance of our approach with the best performing algorithm in the 2016 handwritten document image binarization contest and show that both algorithms perform equally well.

Place, publisher, year, edition, pages
2018. p. 263-268
Keywords [en]
image binarization, recurrent neural networks, Grid LSTM, historical documents, Text analysis, Labeling, Recurrent neural networks, Heuristic algorithms, Training, Degradation, Ink
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:hj:diva-42991DOI: 10.1109/DAS.2018.71ISBN: 978-1-5386-3346-5 (electronic)OAI: oai:DiVA.org:hj-42991DiVA, id: diva2:1288958
Conference
2018 13th IAPR International Workshop on Document Analysis Systems (DAS), vienna
Funder
Knowledge Foundation, 20140032Available from: 2018-07-06 Created: 2019-02-15 Last updated: 2019-08-20Bibliographically approved

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Westphal, FlorianLavesson, NiklasGrahn, Håkan

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