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