This paper introduces a novel approach to spell checking and correction for low-resource and under-represented languages, with a specific focus on an African language, Wolof. By leveraging the capabilities of transformer models and neural networks, we propose an efficient and practical system capable of correcting typos and improving text quality. Our proposed technique involves training a transformer model on a parallel corpus consisting of misspelled sentences and their correctly spelled counterparts, generated using a semi-automatic method. As we fine tune the model to transform misspelled text into accurate sentences, we demonstrate the immense potential of this approach to overcome the challenges faced by resource-scarce and under-represented languages in the realm of spell checking and correction. Our experimental results and evaluations exhibit promising outcomes, offering valuable insights that contribute to the ongoing endeavors aimed at enriching linguistic diversity and inclusion and thus improving digital communication accessibility for languages grappling with scarcity of resources and under-representation in the digital landscape.

This paper presents a spell checker and correction tool specifically designed for Wolof, an under-represented spoken language in Africa. The proposed spell checker leverages a combination of a trie data structure, dynamic programming, and the weighted Levenshtein distance to generate suggestions for misspelled words. We created novel linguistic resources for Wolof, such as a lexicon and a corpus of misspelled words, using a semi-automatic approach that combines manual and automatic annotation methods. Despite the limited data available for the Wolof language, the spell checker’s performance showed a predictive accuracy of 98.31% and a suggestion accuracy of 93.33%.Our primary focus remains the revitalization and preservation of Wolof as an Indigenous and spoken language in Africa, providing our efforts to develop novel linguistic resources. This work represents a valuable contribution to the growth of computational tools and resources for the Wolof language and provides a strong foundation for future studies in the automatic spell checking and correction field.