  The Berber, or Amazigh language family is a low-resource North African
vernacular language spoken by the indigenous Berber ethnic group. It has its
own unique alphabet called Tifinagh used across Berber communities in Morocco,
Algeria, and others. The Afroasiatic language Berber is spoken by 14 million
people, yet lacks adequate representation in education, research, web
applications etc. For instance, there is no option of translation to or from
Amazigh / Berber on Google Translate, which hosts over 100 languages today.
Consequently, we do not find specialized educational apps, L2 (2nd language
learner) acquisition, automated language translation, and remote-access
facilities enabled in Berber. Motivated by this background, we propose a
supervised approach called DaToBS for Detection and Transcription of Berber
Signs. The DaToBS approach entails the automatic recognition and transcription
of Tifinagh characters from signs in photographs of natural environments. This
is achieved by self-creating a corpus of 1862 pre-processed character images;
curating the corpus with human-guided annotation; and feeding it into an OCR
model via the deployment of CNN for deep learning based on computer vision
models. We deploy computer vision modeling (rather than language models)
because there are pictorial symbols in this alphabet, this deployment being a
novel aspect of our work. The DaToBS experimentation and analyses yield over 92
percent accuracy in our research. To the best of our knowledge, ours is among
the first few works in the automated transcription of Berber signs from
roadside images with deep learning, yielding high accuracy. This can pave the
way for developing pedagogical applications in the Berber language, thereby
addressing an important goal of outreach to underrepresented communities via AI
in education.
