Deep Scene Text Detection with Connected Component Proposals

Understand

A growing demand for natural-scene text detection has been witnessed by the computer vision community since text information plays a significant role in scene understanding and image indexing.

• Deep neural networks are being used due to their strong capabilities of pixel-wise classification or word localization, similar to being used in common vision problems.
• In this paper, we present a novel two-task network with integrating bottom and top cues.
• The first task aims to predict a pixel-by-pixel labeling and based on which, word proposals are generated with a canonical connected component analysis.