Advances in Amazigh Language Technologies: A Comprehensive Survey Across Processing Domains

Abstract

The Amazigh language, spoken by millions across North Africa, presents unique computational challenges due to its complex morphological system, dialectal variation, and multiple writing systems. This survey examines technological advances over the past decade across four key domains: natural language processing, speech recognition, optical character recognition, and machine translation. We analyze the evolution from rule-based systems to advanced neural models, demonstrating how researchers have addressed resource constraints through innovative approaches that blend linguistic knowledge with machine learning. Our analysis reveals uneven progress across domains, with optical character recognition reaching high maturity levels while machine translation remains constrained by limited parallel data. Beyond technical metrics, we explore applications in education, cultural preservation, and digital accessibility, showing how these technologies enable Amazigh speakers to participate in the digital age. This work illustrates that advancing language technology for marginalized languages requires fundamentally different approaches that respect linguistic diversity while ensuring digital equity.