eprintid: 10116299
rev_number: 22
eprint_status: archive
userid: 608
dir: disk0/10/11/62/99
datestamp: 2020-12-02 17:03:24
lastmod: 2022-01-27 15:07:39
status_changed: 2020-12-02 17:03:24
type: article
metadata_visibility: show
creators_name: Deng, G
creators_name: Ming, Y
creators_name: Xue, J-H
title: RFRN: A recurrent feature refinement network for accurate and efficient scene text detection
ispublished: pub
divisions: UCL
divisions: B04
divisions: C06
divisions: F61
keywords: Scene text detection; Recurrent segmentation; Feature pyramid network; Feature refinement
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Scene text detection plays a vital role for scene text understanding, but arbitrary-shaped text detection remains a significant challenge. To extract discriminative features, most recent state-of-the-art methods adopt heavy networks, resulting in parameter redundancy and inference inefficiency. For accurate and efficient scene text detection, in this paper we propose a novel recurrent feature refinement network (RFRN). RFRN, as a recurrent segmentation framework, contains a recurrent path augmentation that refines the previous feature maps as inner states, which not only helps improve the segmentation quality, but also fully facilitates the reuse of parameters and low computational cost. During testing, RFRN discards redundant prediction procedures for efficient inference, and achieves a good balance between speed and accuracy of inference. We conduct experiments on four challenging scene text benchmarks, CTW1500, Total-Text, ICDAR2015 and ICDAR2017-MLT, which include curved texts and multi-oriented texts with complex background. The results show that the proposed RFRN achieves competitive performance on detection accuracy while maintaining computational efficiency.
date: 2021-09-17
date_type: published
publisher: Elsevier BV
official_url: https://doi.org/10.1016/j.neucom.2020.10.099
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1832738
doi: 10.1016/j.neucom.2020.10.099
lyricists_name: Xue, Jinghao
lyricists_id: JXUEX60
actors_name: Thomas, Chloe
actors_id: CTHOM59
actors_role: owner
full_text_status: public
publication: Neurocomputing
volume: 453
pagerange: 465-481
citation:        Deng, G;    Ming, Y;    Xue, J-H;      (2021)    RFRN: A recurrent feature refinement network for accurate and efficient scene text detection.                   Neurocomputing , 453    pp. 465-481.    10.1016/j.neucom.2020.10.099 <https://doi.org/10.1016/j.neucom.2020.10.099>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10116299/7/Xue_1-s2.0-S0925231220317124-main.pdf