KhmerST: A Low-Resource Khmer Scene Text Detection and Recognition Benchmark

Vannkinh Nom, Souhail Bakkali, Muhammad Muzzamil Luqman, Mickaël Coustaty, Jean-Marc Ogier

Abstract: Developing effective scene text detection and recognition models hinges on extensive training data, which can be both laborious and costly to obtain, especially for low-resourced languages. Conventional methods tailored for Latin characters often falter with non-Latin scripts due to challenges like character stacking, diacritics, and variable character widths without clear word boundaries. In this paper, we introduce the first Khmer scene-text dataset, featuring 1,544 expert-annotated images, including 997 indoor and 547 outdoor scenes. This diverse dataset includes flat text, raised text, poorly illuminated text, distant and partially obscured text. Annotations provide line-level text and polygonal bounding box coordinates for each scene. The benchmark includes baseline models for scene-text detection and recognition tasks, providing a robust starting point for future research endeavors. The KhmerST dataset is publicly accessible (https://gitlab.com/vannkinhnom123/khmerst).