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A notable example is represented by the task of document layout analysis in handwritten documents, which requires pixel-precise segmentation maps to highlight the different layout components of each document page. These segmentation maps are typically very time-consuming and require a high degree of domain knowledge to be defined, as they are intrinsically characterized by the content of the text. For this reason in the present work, we explore the effects of different initialization strategies for deep learning models employed for this type of task by relying on both in-domain and cross-domain datasets for their pre-training. To test the employed models we use two publicly available datasets with heterogeneous characteristics both regarding their structure as well as the languages of the contained documents. We show how a combination of cross-domain and in-domain transfer learning approaches leads to the best overall performance of the models, as well as speeding up their convergence process.<\/jats:p>","DOI":"10.1007\/s10032-024-00497-4","type":"journal-article","created":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T14:02:36Z","timestamp":1724076156000},"page":"161-175","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["In-domain versus out-of-domain transfer learning for document layout analysis"],"prefix":"10.1007","volume":"28","author":[{"given":"Axel","family":"De Nardin","sequence":"first","affiliation":[]},{"given":"Silvia","family":"Zottin","sequence":"additional","affiliation":[]},{"given":"Claudio","family":"Piciarelli","sequence":"additional","affiliation":[]},{"given":"Gian Luca","family":"Foresti","sequence":"additional","affiliation":[]},{"given":"Emanuela","family":"Colombi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,19]]},"reference":[{"issue":"8","key":"497_CR1","doi-asserted-by":"publisher","first-page":"5517","DOI":"10.1007\/s10462-020-09827-4","volume":"53","author":"SR Narang","year":"2020","unstructured":"Narang, S.R., Jindal, M.K., Kumar, M.: Ancient text recognition: a review. 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