Advancing WordArt-Oriented Scene Text Recognition: Datasets and Methods

WordArt (artistic text) features highly customized fonts,textures, and layouts, making WordArt-oriented scene TExt Recogni-tion (WATER) substantially more challenging than general Scene TextRecognition (STR). Existing STR datasets and methods, typically builtaround regular scene text and fixed-template inputs, struggle to scale toWATER. Thus, we aim to advance this task from both data and modelperspectives. On the data side, we construct a 2M synthetic dataset,WATER-S, with the scale improved by hundreds of times comparedto existing artistic text data. WATER-S consists of two complementarysubsets. One rendered by an upgraded rendering pipeline (SynthWor-dArt), which provides highly accurate and controllable synthetic Wor-dArt data. The other is generated by combining Qwen3-VL for promptmining and Z-Image for image synthesis, which improves the coverage ofrealistic and diverse data. On the model side, we propose WATERec.It adopts a visual encoder supporting arbitrary-shaped inputs and anautoregressive decoder to model complex layouts, structurally breakingthe bottleneck of fixed-template STR on WordArt. Experiments showthat this architecture outperforms prior STR methods, achieving state-of-the-art performance on irregular texts such as WordArt. Togetherwith WATER-R, carefully reorganized from existing real STR data, ourstrong baseline with the new synthetic data and model design reaches90.40% accuracy on WordArt-Bench, surpassing both general-purposeand OCR-specialized vision-language models by a large margin. Codeand data are available at https://github.com/YesianRohn/WATER.