Layout Analysis of Historical Document Images Using a Light Fully Convolutional Network
BP2-STS
- Rahal, Najoua ORCID Université de Fribourg
- Vögtlin, Lars ORCID Université de Fribourg
- Ingold, Rolf ORCID Université de Fribourg
- 2023
Published in:
- Document Analysis and Recognition - ICDAR 2023, LNCS. - 2023, vol. 14191, p. 325-341
English
In the last few years, many deep neural network architec- tures, especially Fully Convolutional Networks (FCN), have been proposed in the literature to perform semantic segmentation. These architectures contain many parameters and layers to obtain good results. However, for Historical document images, we show in this paper that there is no need to use so many trainable parameters. An architecture with much fewer parameters can perform better while being lighter for train- ing than the most popular variants of FCN. To have a fair and complete comparison, qualitative and quantitative evaluations are carried out on various datasets using standard pixel-level metrics.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département d'Informatique
- Language
-
- English
- Classification
- Medicine, Technology, Engineering
- License
-
Rights reserved
- Open access status
- green
- Identifiers
-
- DOI 10.1007/978-3-031-41734-4_20
- ISSN 0302-9743
- ISBN 9783031417337
- Persistent URL
- https://folia.unifr.ch/unifr/documents/334264
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