  Recently, semantic segmentation and general object detection frameworks have
been widely adopted by scene text detecting tasks. However, both of them alone
have obvious shortcomings in practice. In this paper, we propose a novel
end-to-end trainable deep neural network framework, named Pixel-Anchor, which
combines semantic segmentation and SSD in one network by feature sharing and
anchor-level attention mechanism to detect oriented scene text. To deal with
scene text which has large variances in size and aspect ratio, we combine FPN
and ASPP operation as our encoder-decoder structure in the semantic
segmentation part, and propose a novel Adaptive Predictor Layer in the SSD.
Pixel-Anchor detects scene text in a single network forward pass, no complex
post-processing other than an efficient fusion Non-Maximum Suppression is
involved. We have benchmarked the proposed Pixel-Anchor on the public datasets.
Pixel-Anchor outperforms the competing methods in terms of text localization
accuracy and run speed, more specifically, on the ICDAR 2015 dataset, the
proposed algorithm achieves an F-score of 0.8768 at 10 FPS for 960 x 1728
resolution images.
