  In this work, we propose a novel hybrid method for scene text detection
namely Correlation Propagation Network (CPN). It is an end-to-end trainable
framework engined by advanced Convolutional Neural Networks. Our CPN predicts
text objects according to both top-down observations and the bottom-up cues.
Multiple candidate boxes are assembled by a spatial communication mechanism
call Correlation Propagation (CP). The extracted spatial features by CNN are
regarded as node features in a latticed graph and Correlation Propagation
algorithm runs distributively on each node to update the hypothesis of
corresponding object centers. The CP process can flexibly handle scale-varying
and rotated text objects without using predefined bounding box templates.
Benefit from its distributive nature, CPN is computationally efficient and
enjoys a high level of parallelism. Moreover, we introduce deformable
convolution to the backbone network to enhance the adaptability to long texts.
The evaluation on public benchmarks shows that the proposed method achieves
state-of-art performance, and it significantly outperforms the existing methods
for handling multi-scale and multi-oriented text objects with much lower
computation cost.
