Curve Text Detection with Local Segmentation Network and Curve Connection

03/23/2019
by   Zhao Zhou, et al.
0

Curve text or arbitrary shape text is very common in real-world scenarios. In this paper, we propose a novel framework with the local segmentation network (LSN) followed by the curve connection to detect text in horizontal, oriented and curved forms. The LSN is composed of two elements, i.e., proposal generation to get the horizontal rectangle proposals with high overlap with text and text segmentation to find the arbitrary shape text region within proposals. The curve connection is then designed to connect the local mask to the detection results. We conduct experiments using the proposed framework on two real-world curve text detection datasets and demonstrate the effectiveness over previous approaches.

READ FULL TEXT

page 1

page 2

page 3

page 6

research
03/03/2017

Arbitrary-Oriented Scene Text Detection via Rotation Proposals

This paper introduces a novel rotation-based framework for arbitrary-ori...
research
04/11/2021

RayNet: Real-time Scene Arbitrary-shape Text Detection with Multiple Rays

Existing object detection-based text detectors mainly concentrate on det...
research
03/21/2019

Towards Robust Curve Text Detection with Conditional Spatial Expansion

It is challenging to detect curve texts due to their irregular shapes an...
research
10/28/2017

Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition

Text in curve orientation, despite being one of the common text orientat...
research
05/11/2022

Arbitrary Shape Text Detection via Boundary Transformer

Arbitrary shape text detection is a challenging task due to its complexi...
research
12/06/2017

Detecting Curve Text in the Wild: New Dataset and New Solution

Scene text detection has been made great progress in recent years. The d...
research
03/17/2020

Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection

Arbitrary shape text detection is a challenging task due to the high var...

Please sign up or login with your details

Forgot password? Click here to reset