论文标题
全面的基准数据集用于Amharic场景文本检测和识别
Comprehensive Benchmark Datasets for Amharic Scene Text Detection and Recognition
论文作者
论文摘要
埃塞俄比亚/Amharic剧本是最古老的非洲写作系统之一，它在东非至少提供23种语言（例如Amharic，Tigrinya），可容纳1.2亿多人。 Amharic写作系统Abugida具有282个音节，15个标点符号和20个数字。 AMHARIC音节矩阵是从34个基本素数/辅音中得出的，该辅音最多将多达12个适当的变音符号或人声标记添加到字符中。具有常见辅音或人声标记的音节在视觉上可能是相似的，并且会挑战文本识别任务。在这项工作中，我们介绍了第一个名为Hust-Art，Hust-ast，Abe和Tana的全面公共数据集，以在自然场景中进行Amharic脚本检测和认可。我们还进行了广泛的实验，以评估最先进的方法在检测和识别数据集中的Amharic场景文本时的性能。评估结果证明了我们的数据集用于基准测试及其促进强大的AMHARIC脚本检测和识别算法的潜力。因此，结果将使东非的人们受益，包括来自几个国家和国际社区的外交官。
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.
