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A Deep Learning-Based OCR System Implementation for Traceability Ensurement in a Metal Manufacturing Workshop

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Distributed Computing and Artificial Intelligence, Special Sessions III - Intelligent Systems Applications, 21st International Conference (DCAI 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1173))

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Abstract

The traceability of all elements within a metal manufacturing industry constitutes a critical challenge in the digitalization of industrial processes. In this context, the labeling of these elements, coupled with optical character recognition (OCR), plays a significant role. Therefore, in this paper, it is accomplished this challenge by applying three pre-trained deep learning OCR models such as Pytesseract, EasyOCR and KerasOCR to a dataset of images featuring labels with corresponding alphanumeric codes. The performance of these OCR engines has been evaluated using various types of input images, to which preprocessing techniques have been applied to enhance their quality and the legibility of the text contained within. The implementation of these models produced successful results, presenting a viable solution to improve the efficiency and accessibility of information retrieval processes within the industry.

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Acknowledgments

This work has been supported by Centro Mixto de Investigación UDC-Navantia (IN853C 2022/01), funded by GAIN (Xunta de Galicia) and ERDF Galicia 2021-2027.

Míriam Timiraos’s research was supported by the “Xunta de Galicia” through grants to industrial PhD (http://gain.xunta.gal/), under the “Doutoramento Industrial 2022” grant with reference: 04_IN606D_2022_ 2692965.

CITIC, as a center accredited for excellence within the Galician University System and a member of the CIGUS Network, receives subsidies from the Department of Education, Science, Universities, and Vocational Training of the Xunta de Galicia. Additionally, it is co-financed by the EU through the FEDER Galicia 2021-27 operational program (Ref. ED431G 2023/01). Xunta de Galicia. Grants for the consolidation and structuring of competitive research units, GPC (ED431B 2023/49).

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Authors and Affiliations

  1. Department of Industrial Engineering, CTC, CITIC, University of A Coruña, Ferrol, A Coruña, Spain

    Paula Arcano-Bea, Míriam Timiraos, Francisco Zayas-Gato, José Luis Calvo-Rolle & Esteban Jove

  2. Department of Naval and Industrial Engineering, University of A Coruña, Ferrol, Spain

    Pablo Fariñas

Authors
  1. Paula Arcano-Bea
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  2. Míriam Timiraos
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  3. Pablo Fariñas
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  4. Francisco Zayas-Gato
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  5. José Luis Calvo-Rolle
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  6. Esteban Jove
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Corresponding author

Correspondence to Francisco Zayas-Gato .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Francisco Zayas-Gato

  2. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Antonio Díaz-Longueira

  3. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    José-Luis Casteleiro-Roca

  4. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Esteban Jove

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Arcano-Bea, P., Timiraos, M., Fariñas, P., Zayas-Gato, F., Calvo-Rolle, J.L., Jove, E. (2024). A Deep Learning-Based OCR System Implementation for Traceability Ensurement in a Metal Manufacturing Workshop. In: Zayas-Gato, F., Díaz-Longueira, A., Casteleiro-Roca, JL., Jove, E. (eds) Distributed Computing and Artificial Intelligence, Special Sessions III - Intelligent Systems Applications, 21st International Conference. DCAI 2024. Lecture Notes in Networks and Systems, vol 1173. Springer, Cham. https://doi.org/10.1007/978-3-031-73910-1_3

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