DeepAI
Log In Sign Up

Scene Text Detection with Selected Anchor

08/19/2020
by   Anna Zhu, et al.
0

Object proposal technique with dense anchoring scheme for scene text detection were applied frequently to achieve high recall. It results in the significant improvement in accuracy but waste of computational searching, regression and classification. In this paper, we propose an anchor selection-based region proposal network (AS-RPN) using effective selected anchors instead of dense anchors to extract text proposals. The center, scales, aspect ratios and orientations of anchors are learnable instead of fixing, which leads to high recall and greatly reduced numbers of anchors. By replacing the anchor-based RPN in Faster RCNN, the AS-RPN-based Faster RCNN can achieve comparable performance with previous state-of-the-art text detecting approaches on standard benchmarks, including COCO-Text, ICDAR2013, ICDAR2015 and MSRA-TD500 when using single-scale and single model (ResNet50) testing only.

READ FULL TEXT

page 6

page 7

04/24/2018

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

The anchor mechanism of Faster R-CNN and SSD framework is considered not...
01/10/2019

Region Proposal by Guided Anchoring

Region anchors are the cornerstone of modern object detection techniques...
09/28/2020

RRPN++: Guidance Towards More Accurate Scene Text Detection

RRPN is among the outstanding scene text detection approaches, but the m...
09/17/2019

STELA: A Real-Time Scene Text Detector with Learned Anchor

To achieve high coverage of target boxes, a normal strategy of conventio...
07/20/2014

Object Proposal Generation using Two-Stage Cascade SVMs

Object proposal algorithms have shown great promise as a first step for ...
11/12/2017

Feature Enhancement Network: A Refined Scene Text Detector

In this paper, we propose a refined scene text detector with a novel Fea...
12/13/2018

FA-RPN: Floating Region Proposals for Face Detection

We propose a novel approach for generating region proposals for performi...