  Scene text recognition is essential in many applications, including automated
translation, information retrieval, driving assistance, and enhancing
accessibility for individuals with visual impairments. Much research has been
done to improve the accuracy and performance of scene text detection and
recognition models. However, most of this research has been conducted in the
most common languages, English and Chinese. There is a significant gap in
low-resource languages, especially the Swahili Language. Swahili is widely
spoken in East African countries but is still an under-explored language in
scene text recognition. No studies have been focused explicitly on Swahili
natural scene text detection and recognition, and no dataset for Swahili
language scene text detection and recognition is publicly available. We propose
a comprehensive dataset of Swahili scene text images and evaluate the dataset
on different scene text detection and recognition models. The dataset contains
976 images collected in different places and under various circumstances. Each
image has its annotation at the word level. The proposed dataset can also serve
as a benchmark dataset specific to the Swahili language for evaluating and
comparing different approaches and fostering future research endeavors. The
dataset is available on GitHub via this link:
https://github.com/FadilaW/Swahili-STR-Dataset
