  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 Maghrib{\=i} 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.
