Abstract
In this work we highlight the significance of Text Line Detection in documents. By utilizing a well-known Deep Neural Network and by proposing some simple but efficient modifications applied during training of such models, we can achieve very accurate results in different datasets of high diversity. Moreover, such models can be robust even when trained with few data. Our focus is on Greek polytonic documents (typewritten and handwritten) and we provide a new dataset to the public (GTLD-small) for text line detection. We evaluate our method through scenarios applied to the detection and recognition tasks, while demonstrating promising results when compared to popular commercial or open-source systems.
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Acknowledgment
This research has been partially co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE, project Culdile (Cultural Dimensions of Deep Learning, project code: T1EDK-03785), the Operational Program Attica 2014-2020, under the call RESEARCH AND INNOVATION PARTNERSHIPS IN THE REGION OF ATTICA, project reBook (Digital platform for re-publishing Historical Greek Books, project code: ATTP4-0331172) and the project “Corpus-assisted drama translation research: Shakespeare In Translation - ShakeIT”, under the call of internal research project funding of University of Cyprus (UCY https://www.ucy.ac.cy/directory/en/).
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Kaddas, P., Gatos, B., Palaiologos, K., Christopoulou, K., Kritsis, K. (2023). Text Line Detection and Recognition of Greek Polytonic Documents. In: Coustaty, M., Fornés, A. (eds) Document Analysis and Recognition – ICDAR 2023 Workshops. ICDAR 2023. Lecture Notes in Computer Science, vol 14194. Springer, Cham. https://doi.org/10.1007/978-3-031-41501-2_15
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