New Results for the Text Recognition of Arabic Maghribī Manuscripts - Managing an Under-resourced Script - École nationale des chartes Accéder directement au contenu
Pré-Publication, Document De Travail (Preprint/Prepublication) Année : 2022

New Results for the Text Recognition of Arabic Maghribī Manuscripts - Managing an Under-resourced Script

Résumé

HTR models development has become a conventional step for digital humanities projects. The performance of these models, often quite high, relies on manual transcription and numerous handwritten documents. Although the method has proven successful for Latin scripts, a similar amount of data is not yet achievable for scripts considered poorly-endowed, like Arabic scripts. In that respect, we are introducing and assessing a new modus operandi for HTR models development and fine-tuning dedicated to the Arabic Maghribī scripts. The comparison between several state-of-the-art HTR demonstrates the relevance of a word-based neural approach specialized for Arabic, capable to achieve an error rate below 5% with only 10 pages manually transcribed. These results open new perspectives for Arabic scripts processing and more generally for poorly-endowed languages processing. This research is part of the development of RASAM dataset in partnership with the GIS MOMM and the BULAC.
Fichier principal
Vignette du fichier
NewResultsTextReco-2022.pdf (6.77 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03874725 , version 1 (28-11-2022)

Identifiants

Citer

Lucas Noëmie, Clément Salah, Chahan Vidal-Gorène. New Results for the Text Recognition of Arabic Maghribī Manuscripts - Managing an Under-resourced Script. 2022. ⟨hal-03874725⟩
107 Consultations
198 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More