Pixel-Anchor: A Fast Oriented Scene Text Detector with Combined Networks

TL;DR

Pixel-Anchor is a novel end-to-end deep neural network that combines semantic segmentation and SSD to efficiently detect oriented scene text with high accuracy and speed, outperforming existing methods on standard benchmarks.

Contribution

The paper introduces Pixel-Anchor, a unified network integrating semantic segmentation and SSD with feature sharing and attention, improving scene text detection accuracy and efficiency.

Findings

Achieves an F-score of 0.8768 on ICDAR 2015

Runs at 10 FPS for high-resolution images

Outperforms existing methods in accuracy and speed

Abstract

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.