Open Access
| Issue |
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
|
|
|---|---|---|
| Article Number | 00009 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/bioconf/20249700009 | |
| Published online | 05 April 2024 | |
- V. Romero, A. H. Toselli, L. Rodríguez, and E. Vidal, “Computer assisted transcription for ancient text images,” Springer-Verlag Berlin Heidelb., vol. 4633 LNCS, pp. 1182–1193, 2007, DOI: 10.1007/978-3-540-74260-9_105. [Google Scholar]
- G. Carpenè, D. Negrini, B. M. Henry, M. Montagnana, and G. Lippi, “Homocysteine in coronavirus disease (COVID- 19): a systematic literature review,” Diagnosis, vol. 9, no. 3, pp. 306–310, 2022, DOI: 10.1515/dx-2022-0042. [CrossRef] [PubMed] [Google Scholar]
- M. Llamas, M. L. Garo, and L. Giovanella, “Low free-T3 serum levels and prognosis of COVID-19: Systematic review and meta-analysis,” Clin. Chem. Lab. Med., vol. 59, no. 12, pp. 1906–1913, 2021, DOI: 10.1515/cclm-2021-0805. [CrossRef] [PubMed] [Google Scholar]
- A. Savelyev and M. Robbeets, “Bayesian phylolinguistics infers the internal structure and the time-depth of the Turkic language family,” J. Lang. Evol., vol. 5, no. 1, pp. 39–53, 2020, DOI: 10.1093/jole/lzz010. [CrossRef] [Google Scholar]
- N. Shobha Rani, B. J. Bipin Nair, M. Chandrajith, G. Hemantha Kumar, and J. Fortuny, “Restoration of deteriorated text sections in ancient document images using a tri-level semi-adaptive thresholding technique,” Automatika, vol. 63, no. 2, pp. 378–398, 2022, DOI: 10.1080/00051144.2022.2042462. [CrossRef] [Google Scholar]
- Cai, J.; Peng, L.; Tang, Y.; Liu, C.; Li, P. TH-GAN: Generative Adversarial Network Based Transfer Learning for Historical Chinese Character Recognition. In Proceedings of the 2019 International Conference on Document Analysis and Recognition (ICDAR), Sydney, Australia, 20-25 September 2019; pp. 178–183. [CrossRef] [Google Scholar]
- Ziran, Z.; Pic, X.; Innocenti, S.U.; Mugnai, D.; Marinai, S. Text alignment in early printed books combining deep learning and dynamic programming. Pattern Recognit. Lett. 2020, 133, 109–115. [CrossRef] [Google Scholar]
- Watanabe, K.; Takahashi, S.; Kamaya, Y.; Yamada, M.; Mekada, Y.; Hasegawa, J.; Miyazaki, S. Japanese Character Segmentation for Historical Handwritten Official Documents Using Fully Convolutional Networks. In Proceedings of the 2019 International Conference on Document Analysis and Recognition, ICDAR2019, Sydney, Australia, 20-25 September 2019; pp. 934–940. [CrossRef] [Google Scholar]
- Prusty, A.; Aitha, S.; Trivedi, A.; Sarvadevabhatla, S.R.K. Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts. In Proceedings of the 2019 International Conference on Document Analysis and Recognition, ICDAR 2019, Sydney, Australia, 20-25 September 2019; pp. 999–1006. [CrossRef] [Google Scholar]
- Pastor-Pellicer, J.; Afzal, M.Z.; Liwicki, M.; Castro-Bleda, M.J. Complete system for text line extraction using convolutional neural networks and watershed transform. In Proceedings of the 2016 12th IAPR Workshop on Document Analysis Systems (DAS), Santorini, Greece, 11-14 April 2016; pp. 30–35. [CrossRef] [Google Scholar]
- Chen, K.; Seuret, M.; Hennebert, J.; Ingold, R. Convolutional neural networks for page segmentation of historical document images. In Proceedings of the 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan, 9-15 November 2017; Volume 1, pp. 965–970. [CrossRef] [Google Scholar]
- You, A., Kim, J. K., Ryu, I. H., & Yoo, T. K. (2022). Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey. Eye and Vision, 9(1), 1–19.7] ] T. Murphy, “Evaluate open source risks,” http://www.ftponline.com/wss/2002_10/online/tmurphy/, retrieved, vol. 19, p. 2012, 2001. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.