Multilingual NLP has expanded rapidly through large-scale pretraining and cross-lingual transfer, yet this progress remains structurally uneven across writing systems. This survey reframes multilingual NLP around scripts rather than languages, arguing that writing systems constitute an under-theorized axis of computational inequality. Focusing on African scripts — Indigenous (Vai, Ge’ez, Tifinagh), modern (ADLaM, N’Ko), and adapted Arabic-based (Ajami)—we analyze how script properties interact with digital infrastructure, tokenization, and downstream task performance. We organize the literature across four analytical layers: infrastructural (Unicode and input systems), representational (segmentation efficiency and vocabulary allocation), functional (task-level disparities), and epistemic (evaluation bias and the “low-resource” framing). Synthesizing evidence from 47 studies, we show that performance gaps across scripts arise primarily from engineering design choices rather than intrinsic linguistic complexity. We conclude by outlining a research agenda for native multiscript foundation models, including script-aware scaling laws, tokenizer equity metrics, and evaluation reform. We argue that multiscript equity is not a peripheral concern but a structural precondition for genuine multilingual inclusion

Natural Language Processing (NLP) has made significant progress in recent years, largely driven by large-scale pretrained models and vast textual and multimodal corpora. However, these advances remain unevenly distributed, disproportionately benefiting high-resource languages while Indigenous and endangered languages—especially those employing diverse and less widely supported writing systems—remain underrepresented. This paper examines the role of writing system diversity in NLP, with a focus on Indigenous and endangered languages. We propose a theoretical framework that accounts for variation across writing systems and its implications for computational modeling. Specifically, we (i) provide an overview of writing system diversity, (ii) synthesize available computational resources, and (iii) present a structured analysis of challenges in modeling, tokenization, and evaluation.Our analysis shows that writing system diversity reveals structural biases embedded in current NLP pipelines. We conclude by identifying key open challenges and outlining directions for future research toward more inclusive, script-aware NLP approaches that better account for writing system variation.