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Most text detection methods hypothesize texts are horizontal ormulti-oriented and thus define quadrangles as the basic detection unit.However, text in the wild is usually perspectively distorted or curved, whichcan not be easily tackled by existing approaches. In this paper, we propose adeep character embedding network (CENet) which simultaneously predicts thebounding boxes of characters and their embedding vectors, thus making textdetection a simple clustering task in the character embedding space. Theproposed method does not require strong assumptions of forming a straight lineon general text detection, which provides flexibility on arbitrarily curved orperspectively distorted text. For character detection task, a dense predictionsubnetwork is designed to obtain the confidence score and bounding boxes ofcharacters. For character embedding task, a subnet is trained with contrastiveloss to project detected characters into embedding space. The two tasks share abackbone CNN from which the multi-scale feature maps are extracted. The finaltext regions can be easily achieved by a thresholding process on characterconfidence and embedding distance of character pairs. We evaluated our methodon ICDAR13, ICDAR15, MSRA-TD500, and Total-Text. The proposed method achievesstate-of-the-art or comparable performance on all these datasets, and showssubstantial improvement in the irregular-text datasets, i.e. Total-Text.

Detecting Text in the Wild with Deep Character Embedding Network