Comprehensive Benchmark Datasets for Amharic Scene Text Detection and Recognition

TL;DR

This paper introduces the first comprehensive public datasets for Amharic scene text detection and recognition, enabling benchmarking and advancing research in recognizing Amharic script in natural scenes.

Contribution

It presents new datasets (HUST-ART, HUST-AST, ABE, Tana) for Amharic text detection and recognition, and evaluates state-of-the-art methods on these datasets.

Findings

Datasets are robust for benchmarking Amharic text recognition.

State-of-the-art methods show promising performance on the datasets.

The datasets facilitate future development of Amharic OCR algorithms.

Abstract

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.