  Ethiopic/Amharic script is one of the oldest African writing systems, which
serves at least 23 languages (e.g., Amharic, Tigrinya) in East Africa for more
than 120 million people. The Amharic writing system, Abugida, has 282
syllables, 15 punctuation marks, and 20 numerals. The Amharic syllabic matrix
is derived from 34 base graphemes/consonants by adding up to 12 appropriate
diacritics or vocalic markers to the characters. The syllables with a common
consonant or vocalic markers are likely to be visually similar and challenge
text recognition tasks. In this work, we presented the first comprehensive
public datasets named HUST-ART, HUST-AST, ABE, and Tana for Amharic script
detection and recognition in the natural scene. We have also conducted
extensive experiments to evaluate the performance of the state of art methods
in detecting and recognizing Amharic scene text on our datasets. The evaluation
results demonstrate the robustness of our datasets for benchmarking and its
potential of promoting the development of robust Amharic script detection and
recognition algorithms. Consequently, the outcome will benefit people in East
Africa, including diplomats from several countries and international
communities.
