TY - JOUR
T1 - A review of natural scene text detection methods
AU - Yang, Lingqian
AU - Ergu, Daji
AU - Cai, Ying
AU - Liu, Fangyao
AU - Ma, Bo
N1 - Publisher Copyright:
© 2021 The Authors. Published by Elsevier B.V.
PY - 2021
Y1 - 2021
N2 - Natural scene text detection has an important role to play in getting textual information from natural scenes. With the continuous development of deep learning, natural scene text detection methods are emerging and achieving better results on detection tasks. In this paper, analysis, and summary of the current stage of deep learning-based text algorithms for natural scenes, can be divided into two types: region of the proposal and semantic segmentation, and the content of these two series of related algorithms is described. Secondly, a publicly available dataset and detection performance metrics for scene text detection are presented. Ultimately, the research in scene text detection is summarized and looked forward to in the hope of providing new research directions for subsequent algorithms.
AB - Natural scene text detection has an important role to play in getting textual information from natural scenes. With the continuous development of deep learning, natural scene text detection methods are emerging and achieving better results on detection tasks. In this paper, analysis, and summary of the current stage of deep learning-based text algorithms for natural scenes, can be divided into two types: region of the proposal and semantic segmentation, and the content of these two series of related algorithms is described. Secondly, a publicly available dataset and detection performance metrics for scene text detection are presented. Ultimately, the research in scene text detection is summarized and looked forward to in the hope of providing new research directions for subsequent algorithms.
KW - Deep learning
KW - Nature scenes
KW - Text detection
UR - http://www.scopus.com/inward/record.url?scp=85124944371&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2022.01.185
DO - 10.1016/j.procs.2022.01.185
M3 - Conference article
AN - SCOPUS:85124944371
SN - 1877-0509
VL - 199
SP - 1458
EP - 1465
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 8th International Conference on Information Technology and Quantitative Management, ITQM 2020 and 2021
Y2 - 9 July 2021 through 11 July 2021
ER -