Detecting Text in the Wild with Deep Character Embedding Network

Understand

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.