A Modular and Automated Annotation Platform for Handwritings: Evaluation on Under-Resourced Languages - École nationale des chartes
Chapitre D'ouvrage Année : 2021

A Modular and Automated Annotation Platform for Handwritings: Evaluation on Under-Resourced Languages

Chahan Vidal-Gorène
Boris Dupin
  • Fonction : Auteur
Aliénor Decours-Perez
  • Fonction : Auteur
Thomas Riccioli
  • Fonction : Auteur

Résumé

There is today several approaches for automatic handwritten document analysis. HTR achieve in particular convincing results both in layout analysis and text recognition, but also in more up-to-date requests like name entity-recognition, script identification or manuscript datation. These systems are trained and evaluated with large open and specialized databases. Manual annotation and proofreading of handwritten documents is a key step to train such systems. However, it is a time-consuming task, especially when the formats required by the systems display considerable variations, or when the interfaces do not manage several level of information. We propose a new modular and collaborative interface online, ready-to-use, for multilevel annotation and quick-view solution for handwritten and printed documents, including for right-to-left languages. This interface undertakes the creation of customized projects, and the management, the conversion and the export of data in the different formats and standards of the state-of-the-art. It includes automated tasks for layout analysis and text lines extraction with high level fine-tuning capacities. We present this new interface through the case study of the creation of a database for Armenian, an under-resourced language with specific paleographical issues.
Fichier non déposé

Dates et versions

hal-03971691 , version 1 (03-02-2023)

Identifiants

Citer

Chahan Vidal-Gorène, Boris Dupin, Aliénor Decours-Perez, Thomas Riccioli. A Modular and Automated Annotation Platform for Handwritings: Evaluation on Under-Resourced Languages. Lladós, J.; Lopresti, D.; Uchida, S. Document Analysis and Recognition – ICDAR 2021, 12823, Springer International Publishing, pp.507-522, 2021, Lecture Notes in Computer Science, ⟨10.1007/978-3-030-86334-0_33⟩. ⟨hal-03971691⟩
37 Consultations
0 Téléchargements

Altmetric

Partager

More