The anchor mechanism of Faster R-CNN and SSD framework is considered not
effective enough to scene text detection, which can be attributed to its IoU
based matching criterion between anchors and ground-truth boxes. In order to
better enclose scene text instances of various shapes, it requires to design
anchors of various scales, aspect ratios and even orientations manually, which
makes anchor-based methods sophisticated and inefficient. In this paper, we
propose a novel anchor-free region proposal network (AF-RPN) to replace the
original anchor-based RPN in the Faster R-CNN framework to address the above
problem. Compared with a vanilla RPN and FPN-RPN, AF-RPN can get rid of
complicated anchor design and achieve higher recall rate on large-scale
COCO-Text dataset. Owing to the high-quality text proposals, our Faster R-CNN
based two-stage text detection approach achieves state-of-the-art results on
ICDAR-2017 MLT, ICDAR-2015 and ICDAR-2013 text detection benchmarks when using
single-scale and single-model (ResNet50) testing only.

本论文提出了一种新的基于区域提案网络的方法来替换 Faster R-CNN 中的锚点机制，通过去除复杂的锚点设计，可以在大规模 COCO-Text 数据集上实现更高的召回率，并在 ICDAR-2017 MLT、ICDAR-2015 和 ICDAR-2013 文本检测基准测试中取得最先进的结果。

一种基于 Faster R-CNN 的自由锚点区域提议网络用于文本检测方法

An Anchor-Free Region Proposal Network for Faster R-CNN based Text  Detection Approaches

This paper describes the COCO-Text dataset. In recent years large-scale
datasets like SUN and Imagenet drove the advancement of scene understanding and
object recognition. The goal of COCO-Text is to advance state-of-the-art in
text detection and recognition in natural images. The dataset is based on the
MS COCO dataset, which contains images of complex everyday scenes. The images
were not collected with text in mind and thus contain a broad variety of text
instances. To reflect the diversity of text in natural scenes, we annotate text
with (a) location in terms of a bounding box, (b) fine-grained classification
into machine printed text and handwritten text, (c) classification into legible
and illegible text, (d) script of the text and (e) transcriptions of legible
text. The dataset contains over 173k text annotations in over 63k images. We
provide a statistical analysis of the accuracy of our annotations. In addition,
we present an analysis of three leading state-of-the-art photo Optical
Character Recognition (OCR) approaches on our dataset. While scene text
detection and recognition enjoys strong advances in recent years, we identify
significant shortcomings motivating future work.

本文介绍了 COCO-Text 数据集，该数据集基于 MS COCO 数据集，旨在推进自然图像的文本检测和识别。数据集中包含超过 173,000 个文本注释和超过 63,000 张图像，文本注释覆盖了文本的边界框、机器印刷文本和手写文本的分类、易读和难读文本的分类、文本的字体和可读文本的转录。本文还提供了数据集注释的准确性统计分析，并对三种最先进的光学字符识别方法在数据集上的表现进行了分析，结果表明文本检测和识别存在显著的不足，需要进一步研究。