A Smart Translation System between Hausa and English Language Text Using Natural Language Processing Techniques

This paper developed a smart system for translating Hausa and English language texts using Natural Language Processing (NLP) techniques to address gaps in existing translation platforms. Existing translation systems have difficulties in accurately handling Hausa language idioms and dialect variations, and their reliance on continuous internet connectivity makes them less effective in areas with poor network access. To overcome these challenges, the system leverages mBART, Python, Flask API, and MySQL for storage, with a user- friendly interface built in Tkinter and web technologies and packaged for desktop development via PyInstaller. The agile development methodology enabled iterative improvements through user feedback, while data for model training were drawn from secondary resources (JW300, OPUS, Masakhane, and Hausa media). Key features include bidirectional translation, offline functionality, translation history, and a feedback mechanism for continual learning. Results show improved handling of Hausa’s language complex morphology, dialectal variation, and context-dependent meanings compared to general- purpose systems. The system provides accurate and reliable translations while supporting offline functionality. The outcomes also show a user-friendly translation interface, with clarity, ease of use features and a translation history. The study recommends that Hausa-to- English language datasets be broadened, pragmatic linguistic elements be incorporated to better interpret idioms, multimodal input capabilities be added, and continuous user feedback mechanisms be enhanced. This paper contributed to the field of NLP by providing a practical solution to the translating Hausa-English language text-specific challenges. It also supports digital inclusion and advances the development of machine translation for African languages.