Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Comprehensive Framework for Industrial Sticker Information Recognition using Advanced OCR and Object Detection Techniques

Gabriella Rabelo Monteiro
* ,
Leonardo Yuto Camelo
ORCID logo ,
Gustavo Aquino Aquino
ORCID logo ,
Rubens de Andrade Fernandes
ORCID logo ,
Raimundo Cláudio Souza Gomes
ORCID logo ,
André Luiz Printes
,
Israel Gondres Torné
ORCID logo ,
Nádila Silva Azevedo
,
Carlos Mauricio Serodio Figueiredo
,
Heitor Lifsitch Nogueira da Silva
ORCID logo ,
Jozias Parente Oliveira
Version 1 : Received: 10 May 2023 / Approved: 11 May 2023 / Online: 11 May 2023 (13:33:45 CEST)

A peer-reviewed article of this Preprint also exists.

Monteiro, G.; Camelo, L.; Aquino, G.; Fernandes, R.A.; Gomes, R.; Printes, A.; Torné, I.; Silva, H.; Oliveira, J.; Figueiredo, C. A Comprehensive Framework for Industrial Sticker Information Recognition Using Advanced OCR and Object Detection Techniques. Appl. Sci. 2023, 13, 7320. Monteiro, G.; Camelo, L.; Aquino, G.; Fernandes, R.A.; Gomes, R.; Printes, A.; Torné, I.; Silva, H.; Oliveira, J.; Figueiredo, C. A Comprehensive Framework for Industrial Sticker Information Recognition Using Advanced OCR and Object Detection Techniques. Appl. Sci. 2023, 13, 7320.

Abstract

Recent advancements in Artificial Intelligence (AI), Deep Learning (DL), and computer vision have revolutionized various industrial processes through image classification and object detection. State-of-the-art Optical Character Recognition (OCR) and Object Detection (OD) technologies, such as YOLO and PaddleOCR, have emerged as powerful solutions for addressing challenges in recognizing textual and non-textual information on printed stickers. However, a well-established framework integrating these cutting-edge technologies for industrial applications still needs to be discovered. In this paper, we propose an innovative framework that combines advanced OCR and OD techniques to automate visual inspection processes in an industrial context. Our primary contribution is a comprehensive framework adept at detecting and recognizing textual and non-textual information on printed stickers within a company, harnessing the latest AI tools and technologies for sticker information recognition. Our experiments reveal an overall macro accuracy of 0.88 for the sticker OCR across three distinct patterns. Furthermore, the proposed system goes beyond traditional Printed Character Recognition (PCR) by extracting supplementary information, such as barcodes and QR codes present in the image, significantly streamlining industrial workflows and minimizing manual labor demands.

Keywords

Optical Character Recognition; Sticker Pattern; Deep Learning; Object Detection; YOLO; Manufacture Automation; Paddle OCR

Subject

Computer Science and Mathematics, Computer Vision and Graphics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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