Text Line Extraction Using Fully Convolutional Network and Energy Minimization-Reference-Cited by-同舟云学术

Text Line Extraction Using Fully Convolutional Network and Energy Minimization

Published:2021 Issue: Volume: Page:126-140
ISSN:0302-9743
Container-title:Pattern Recognition. ICPR International Workshops and Challenges
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Author:

Barakat Berat Kurar^ORCID,Droby Ahmad,Alaasam Reem,Madi Boraq,Rabaev Irina,El-Sana Jihad

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-68787-8_9

Reference30 articles.

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2. Aldavert, D., Rusiñol, M.: Manuscript text line detection and segmentation using second-order derivatives. In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp. 293–298. IEEE (2018)

3. Barakat, B., Droby, A., Kassis, M., El-Sana, J.: Text line segmentation for challenging handwritten document images using fully convolutional network. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 374–379. IEEE (2018)

4. Barakat, B., El-Sana, J.: Binarization free layout analysis for Arabic historical documents using fully convolutional networks. In: 2nd International Workshop on Arabic Script Analysis and Recognition (ASAR), pp. 26–30. IEEE (2018)

5. Barakat, B.K., Cohen, R., El-Sana, J., Rabaev, I.: VML-MOC: segmenting a multiply oriented and curved handwritten text line dataset. In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), vol. 6, pp. 13–18. IEEE (2019)

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