DH Benelux Journal 2. Digital Humanities in Society

The second volume of the DH Benelux Journal. This volume includes four full-length, peer-reviewed articles that are based on accepted contributions to the 2019 DH Benelux conference in Liège (Belgium) on Digital Humanities in Society. Contents: 1. Editors' Preface (Wout Dillen, Marijn Koolen, Marieke van Erp); 2. Introduction: Digital Humanities in Society (Ingrid Mayeur and Claartje Rasterhoff); 3. A Corpus-Based Approach to Michelangelo’s Epistolary Language (GianlucaValenti); 4. The Datafication of Early Modern Ordinances (C. Annemieke Romein, Sara Veldhoen, and Michel de Gruijter); 5. A-poetic Technology. #GraphPoem and the Social Function of Computational Performance (Chris Tanasescu, Diana Inkpen, Vaibhav Kesarwani, and Prasadith Kirinde Gamaarachchige); 6. Decomplexifying the network pipeline: a tool for RDF/Wikidata to network analysis (Julie M. Birkholz and Albert Meroo-Peuel

Reading order detection on handwritten documents

[EN] Recent advances in Handwritten Text Recognition and Document Layout Analysis have made it possible to convert digital images of manuscripts into electronic text. However, providing this text with the correct structure and context is still an open problem that needs to be solved to actually enable extracting the relevant information conveyed by the text. The most important structure needed for a set of text elements is their reading order. Most of the studies on the reading order problem are rule-based approaches and focus on printed documents. Much less attention has been paid so far to handwritten text documents, where the problem becomes particularly important-and challenging. In this work, we propose a new approach to automatically determine the reading order of text regions and lines in handwritten text documents. The task is approached as a sorting problem where the order-relation operator is automatically learned from examples. We experimentally demonstrate the effectiveness of our method on three different datasets at different hierarchical levels.The authors want to thank to the Centre de Recerca d'Història Rural, the National Archives of Finland and Déjean Hervé for facilitating the datasets used in this work, and to Juan Miguel Vilar for the enlighment comments. Also, this work was partially supported by Universitat Politècnica de València under grant FPI-II/900, by Generalitat Valenciana under the EU-FEDER Comunitat Valenciana 2014-2020 grant IDIFEDER/2018/025 'Sistemas de fabricación inteligente para la indústria 4.0', by the Ministerio de Ciencia, Innovación y Universidades project DocTIUM (Ref. RTI2018-095645-B-C22), by the BBVA Foundation through the 2019 Digital Humanities research grant 'HistWeather ' Dos Siglos de Datos Climáticos' and by the Agencia Estatal de Investigación under project SimancasSearch (PID2020-116813RB-I00).Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Quirós, L.; Vidal, E. (2022). Reading order detection on handwritten documents. Neural Computing and Applications. 34(12):9593-9611. https://doi.org/10.1007/s00521-022-06948-5959396113412Ares Oliveira S, Seguin B, Kaplan F (2018) dhSegment: A generic deep-learning approach for document segmentation. CoRR abs/1804.10371Bluche T (2015) Deep neural networks for large vocabulary handwritten text recognition. Ph.D. thesis, Ecole Doctorale Informatique de Paris-Sud - Laboratoire d’Informatique pour la Mécanique et les Sciences de l’Ingénieur . Discipline : informatiqueBreuel TM (2003) High performance document layout analysis. 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