Abstract
Semantic segmentation models have shown impressive performance in the context of historical document layout analysis, but their effectiveness is reliant on having access to a large number of high-quality annotated images for training. A popular approach to address the lack of training data in other domains is to rely on transfer learning to transfer the knowledge learned from a large-scale, general-purpose dataset (e.g. ImageNet) to a domain-specific task. However, this approach has been shown to lead to unsatisfactory results when the target task is completely unrelated to the data employed for the pre-training process, which is the case when working on document layout analysis. For this reason, in the present paper, we provide an overview of domain-specific transfer learning for document layout segmentation. In particular, we show how relying on document-related images for the pre-training process leads to consistently improved performance and faster convergence compared to training from scratch or even relying on a large, general purpose, dataset such as ImageNet.
A. De Nardin and S. Zottin—Equally contributed.
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Acknowledgements
Partial financial support was received from Piano Nazionale di Ripresa e Resilienza (PNRR) DD 3277 del 30 dicembre 2021 (PNRR Missione 4, Componente 2, Investimento 1.5) - Interconnected Nord-Est Innovation Ecosystem (iNEST).
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De Nardin, A., Zottin, S., Colombi, E., Piciarelli, C., Foresti, G.L. (2024). Is ImageNet Always the Best Option? An Overview on Transfer Learning Strategies for Document Layout Analysis. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023. Lecture Notes in Computer Science, vol 14366. Springer, Cham. https://doi.org/10.1007/978-3-031-51026-7_41
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