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Synthetic data has been a critical tool for training scene text detection andrecognition models. On the one hand, synthetic word images have proven to be asuccessful substitute for real images in training scene text recognizers. Onthe other hand, however, scene text detectors still heavily rely on a largeamount of manually annotated real-world images, which are expensive. In thispaper, we introduce UnrealText, an efficient image synthesis method thatrenders realistic images via a 3D graphics engine. 3D synthetic engine providesrealistic appearance by rendering scene and text as a whole, and allows forbetter text region proposals with access to precise scene information, e.g.normal and even object meshes. The comprehensive experiments verify itseffectiveness on both scene text detection and recognition. We also generate amultilingual version for future research into multilingual scene text detectionand recognition. Additionally, we re-annotate scene text recognition datasetsin a case-sensitive way and include punctuation marks for more comprehensiveevaluations. The code and the generated datasets are released at:https://github.com/Jyouhou/UnrealText/ .

UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World