Digital reproductions of historical documents from Late Antiquity to early medieval Europe contain annotations in handwritten graphic symbols or signs. The study of such symbols may potentially reveal essential insights into the social and historical context. However, finding such symbols in handwritten documents is not an easy task, requiring the knowledge and skills of expert users, i.e., paleographers. An AI-based system can be designed, highlighting potential symbols to be validated and enriched by the experts, whose decisions are used to improve the detection performance. This paper shows how this task can benefit from feature auto-encoding, showing how detection performance improves with respect to trivial template matching.

Accurate Graphic Symbol Detection in Ancient Document Digital Reproductions / Ziran, Zahra; Bernasconi, Eleonora; Ghignoli, Antonella; Leotta, Francesco; Mecella, Massimo. - 12916:(2021), pp. 147-162. ((Intervento presentato al convegno 16th International Conference on Document Analysis and Recognition ICDAR 2021 tenutosi a Losanna, Svizzera [10.1007/978-3-030-86198-8_12].

Accurate Graphic Symbol Detection in Ancient Document Digital Reproductions

Ziran, Zahra
Investigation
;
Bernasconi, Eleonora
Membro del Collaboration Group
;
Ghignoli, Antonella
Supervision
;
Leotta, Francesco
Writing – Review & Editing
;
Mecella, Massimo
Supervision
2021

Abstract

Digital reproductions of historical documents from Late Antiquity to early medieval Europe contain annotations in handwritten graphic symbols or signs. The study of such symbols may potentially reveal essential insights into the social and historical context. However, finding such symbols in handwritten documents is not an easy task, requiring the knowledge and skills of expert users, i.e., paleographers. An AI-based system can be designed, highlighting potential symbols to be validated and enriched by the experts, whose decisions are used to improve the detection performance. This paper shows how this task can benefit from feature auto-encoding, showing how detection performance improves with respect to trivial template matching.
978-3-030-86197-1
978-3-030-86198-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1567991
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