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HTR models development has become a conventional step for digital humanitiesprojects. The performance of these models, often quite high, relies on manualtranscription and numerous handwritten documents. Although the method hasproven successful for Latin scripts, a similar amount of data is not yetachievable for scripts considered poorly-endowed, like Arabic scripts. In thatrespect, we are introducing and assessing a new modus operandi for HTR modelsdevelopment and fine-tuning dedicated to the Arabic Maghrib{\=i} scripts. Thecomparison between several state-of-the-art HTR demonstrates the relevance of aword-based neural approach specialized for Arabic, capable to achieve an errorrate below 5% with only 10 pages manually transcribed. These results open newperspectives for Arabic scripts processing and more generally forpoorly-endowed languages processing. This research is part of the developmentof RASAM dataset in partnership with the GIS MOMM and the BULAC.

New Results for the Text Recognition of Arabic Maghrib{\=i} Manuscripts -- Managing an Under-resourced Script