Through this paper, we seek to reduce the communication barrier between the hearing-impaired community and the larger society who are usually not familiar with sign language in the sub-Saharan region of Africa with the largest occurrences of hearing disability cases, while using Nigeria as a case study. The dataset is a pioneer dataset for the Nigerian Sign Language and was created in collaboration with relevant stakeholders. We pre-processed the data in readiness for two different object detection models and a classification model and employed diverse evaluation metrics to gauge model performance on sign-language to text conversion tasks. Finally, we convert the predicted sign texts to speech and deploy the best performing model in a lightweight application that works in real-time and achieves impressive results converting sign words/phrases to text and subsequently, into speech.

翻译：通过这份文件,我们力求减少听障社区与广大社会之间的沟通障碍,他们通常不熟悉手语,在非洲撒哈拉以南地区,他们通常不熟悉手语,他们审理残疾案件最多,同时利用尼日利亚作为案例研究,数据集是尼日利亚手语的先锋数据集,是与相关利益攸关方合作创建的。我们预先处理数据,准备用于两种不同的物体探测模型和分类模型,并采用多种评价指标,以衡量手语示范性能和文本转换任务。最后,我们将预测的手语文本转换为语言,在轻量级应用中采用最佳表现模式,实时工作,并取得令人印象深刻的成果,将手语/文字转换为文字,随后转换为语言。