An ID Badge Information Extractor Based on Object Detection and Optical Character Recognition

被引：1

作者：

Cavalcante, Wallace ^{[1
]}

Torne, Israel Gondres ^{[1
]}

Camelo, Leonardo ^{[1
]}

Fernandes, Rubens ^{[1
]}

Printes, Andre ^{[1
]}

Braganca, Hendrio ^{[1
]}

机构：

[1] Amazonas State Univ, Embedded Syst Lab, BR-69050020 Manaus, Brazil

来源：

IEEE ACCESS | 2024年 / 12卷

关键词：

Optical character recognition; Data mining; YOLO; Accuracy; Object recognition; Measurement; Image segmentation; Computer vision; Text recognition; Text detection; Artificial intelligence; You Only Look Once; optical character recognition; deep learning; object detection;

D O I：

10.1109/ACCESS.2024.3471449

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Advancements in Artificial Intelligence and Deep Learning have impacted numerous fields, particularly through innovations like You Only Look Once for object detection and PaddleOCR for optical character recognition in computer vision. These technologies are pivotal in automating and enhancing the accuracy of tasks, such as detecting and extracting characters from identification badges, which have traditionally been prone to manual effort, time consumption, and errors. This study introduces an automated method for detecting and extracting textual data from identification badges, achieving notable efficiency and precision. Our results indicate a Character Error Rate of 0.028 for name recognition and a flawless score for registration number extraction, with a precision rate of 0.992 for the identification badge detection model. By highlighting the importance of automating character extraction and badge detection, this study showcases the ability of Artificial Intelligence and Deep Learning to revolutionize and improve data extraction processes with digital identification systems in professional environments.

引用

页码：152559 / 152567

页数：9

共 40 条

[1] Digital photo hoarding in online retail context. An in-depth qualitative investigation of retail consumers [J].

Agarwal, Reeti ;

Mehrotra, Ankit ;

Pant, Manoj Kumar ;

Alzeiby, Ebtesam Abdullah ;

Vishnoi, Sushant Kumar .

JOURNAL OF RETAILING AND CONSUMER SERVICES, 2024, 78

[2]

Arlazarov V. L., 2022, Pattern Recognit. Image Anal., V32

[3]

Bhatt C., 2023, P 3 INT C SEC CYB CO, P703, DOI [10.1109/ICSCCC58608.2023.10176809, DOI 10.1109/ICSCCC58608.2023.10176809]

[4]

Bisong E., 2019, Building Machine Learning and Deep Learning Models on Google Cloud Platform, P59, DOI [10.1007/978-1-4842-4470-8_7, DOI 10.1007/978-1-4842-4470-8_7, 10.1007/978-1-4842-4470-87]

[5] Deep learning in computer vision: A critical review of emerging techniques and application scenarios [J].

Chai, Junyi ;

Zeng, Hao ;

Li, Anming ;

Ngai, Eric W. T. .

MACHINE LEARNING WITH APPLICATIONS, 2021, 6

[6]

Ciaglia F, 2022, Arxiv, DOI arXiv:2211.13523

[7] Object detection using YOLO: challenges, architectural successors, datasets and applications [J].

Diwan, Tausif ;

Anirudh, G. ;

Tembhurne, Jitendra, V .

MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (06) :9243-9275

[8] The indexing and retrieval of document images: A survey [J].

Doermann, D .

COMPUTER VISION AND IMAGE UNDERSTANDING, 1998, 70 (03) :287-298

[9]

Du YN, 2020, Arxiv, DOI [arXiv:2009.09941, DOI 10.48550/ARXIV.2009.09941]

[10]

Gulnara B., 2022, P INT C SMART INF SY, P1, DOI [10.1109/SIST54437.2022.9945823, DOI 10.1109/SIST54437.2022.9945823]

← 1 2 3 4 →