Go to DBLP homepageICDAR 2024 Competition on Artistic Text Recognition
Abstract: Artistic text is widely used in advertisements, slogans, exhibitions, decorations, magazines, and books. However, artistic text recognition is an overlooked and extremely challenging task with importance and practicability in various applications. Artistic text recognition often has several challenges such as the various appearances with special-designed fonts and effects, the complex connections and overlaps between characters, and the severe interference from background patterns. Therefore, we organized the ICDAR 2024 Competition on Artistic Text Recognition to invite participants to solve these challenges. We propose the WordArt-V1.5 dataset to advance the field by incorporating a broader range of artistic text images sourced from diverse scenes. This enhanced artistic text recognition dataset contains a total of 12,000 images with 6,000 for training and 6,000 for testing. The competition attracted 33 participants and received 126 submissions with a best accuracy of 91.07%. In this paper, we provide an overview of the competition, detailing the proposed dataset, task, evaluation protocol, and result summaries.
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