A comprehensive scheme for tattoo text detection

doi:10.1016/j.patrec.2022.10.007

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 模式分析与学习团队

	A comprehensive scheme for tattoo text detection
	Banerjee, Ayan 2; Shivakumara, Palaiahnakote 3; Pal, Umapada 2; Raghavendra, Ramachandra 4; Liu, Cheng-Lin 1,5
刊名	PATTERN RECOGNITION LETTERS
	2022-11-01
卷号	163 页码:168-179
ISSN号	0167-8655
DOI	10.1016/j.patrec.2022.10.007
通讯作者	Raghavendra, Ramachandra(raghavendra.ramachandra@ntnu.no)
英文摘要	Tattoo text detection provides a vital clue for person and crime identification. Due to the freestyle and unconstrained nature of handwritten tattoo text over skin regions, accurate tattoo text detection is very challenging. This paper proposes a comprehensive scheme for tattoo text detection which comprises (a) adaptive Deformable Convolutional Neural Network (DCNN) for skin region detection to reduce text detection complexity (b) a Decoupled Gradient Text Detector (DGTD) for tattoo text detection from skin region (c) a Deep Q-Network (DQN) to refine the bounding boxes detected by DGTD, and (d) a Term -Frequency-Inverse-Document-Frequency (TF-IDF) model to group the words into text lines based on se-mantic information to fix the bounding box for the line. To test the effectiveness, the proposed method is evaluated on different datasets, namely, (i) a newly developed tattoo text dataset, (ii) benchmark bib number dataset of the marathon, and (iii) person re-identification dataset. The proposed method achieves 91.2, 87.5, and 88.8 F-scores from these three respective datasets. To demonstrate its superior performance, the text detection module (without skin detection) is also compared with state-of-the-art scene text detection methods on benchmark datasets, namely, ICDAR 2019 ArT, Total-Text, and DAST1500 and the proposed method achieves 90.3, 88.5 and 89.8 F-score from these respective datasets. (c) 2022 The Authors. Published by Elsevier B.V.
资助项目	Ministry of Higher Education of Malaysia[FRGS/1/2020/ICT02/UM/02/4] ; TIH, ISI Kolkata
WOS关键词	RECOGNITION
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000877215000013
资助机构	Ministry of Higher Education of Malaysia ; TIH, ISI Kolkata
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50563]
专题	自动化研究所_模式识别国家重点实验室_模式分析与学习团队
通讯作者	Raghavendra, Ramachandra
作者单位	1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 2.Indian Stat Inst, Comp Vis & Pattern Recognit Unit, Kolkata, India 3.Univ Malaya, Dept Comp Syst & Technol, Kula Lumpur, Malaysia 4.NTNU, Fac Informat Technol & Elect Engn, IIK, Oslo, Norway 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Banerjee, Ayan,Shivakumara, Palaiahnakote,Pal, Umapada,et al. A comprehensive scheme for tattoo text detection[J]. PATTERN RECOGNITION LETTERS,2022,163:168-179.
APA	Banerjee, Ayan,Shivakumara, Palaiahnakote,Pal, Umapada,Raghavendra, Ramachandra,&Liu, Cheng-Lin.(2022).A comprehensive scheme for tattoo text detection.PATTERN RECOGNITION LETTERS,163,168-179.
MLA	Banerjee, Ayan,et al."A comprehensive scheme for tattoo text detection".PATTERN RECOGNITION LETTERS 163(2022):168-179.