Comprehensive Benchmark Datasets for Amharic Scene Text Detection and Recognition
This addresses a data scarcity problem for researchers and developers working on Amharic script, benefiting over 120 million people in East Africa, though it is incremental as it focuses on dataset creation rather than new algorithms.
The authors tackled the lack of public datasets for Amharic scene text detection and recognition by presenting the first comprehensive datasets (HUST-ART, HUST-AST, ABE, Tana) and evaluated state-of-the-art methods on them, demonstrating their robustness for benchmarking.
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.