2019-10-15-huang19c.md

title	crossref	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
An Anchor-Free Oriented Text Detector with Connectionist Text Proposal Network	acml19	Deep learning approaches have made great progress for the scene text detection in recent years. However, there are still some difficulties such as the text orientation and varying aspect ratios. In this paper, we address these issues by treating a text instance as a sequence of fine-scale proposals. The vertical distances from a text pixel to the text borders are directly regressed without the commonly used anchor mechanism, and then the small local proposals are connected during the post-processing. A U-shape convolutional neural network (CNN) architecture is used to incorporate the context information and detect small text instances. In experiments, the proposed approach, referred to as Anchor-Free oriented text detector with Connectionist Text Proposal Network (AFCTPN), achieves better or comparable performance with less time consumption on benchmark datasets.	inproceedings	Proceedings of Machine Learning Research	huang19c	0	An Anchor-Free Oriented Text Detector with Connectionist Text Proposal Network	631	645	631-645	631	false	Huang, Chenhui and Xu, Jinhua	given family Chenhui Huang given family Jinhua Xu	2019-10-15		PMLR	Proceedings of The Eleventh Asian Conference on Machine Learning	101	inproceedings	date-parts 2019 10 15	http://proceedings.mlr.press/v101/huang19c/huang19c.pdf