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Machine-based identification system via optical character recognition

Author

Listed:
  • Mohammad Shahin

    (The University of Texas at San Antonio)

  • F. Frank Chen

    (The University of Texas at San Antonio)

  • Ali Hosseinzadeh

    (The University of Texas at San Antonio)

Abstract

In the past, information technology was frequently considered a waste from Lean manufacturing perspective. Though the business landscape evolves and competition from low-cost nations grows, new models must be created that provides a competitive edge by combining the Lean paradigm with Industry 4.0 technical advancements. This paper aims to contribute to this field by assessing the supporting function of a Machine-based Identification system (MBID) via Optical Character Recognition (OCR) in Lean manufacturing paradigm. The objective of this paper is to also explore the use of MBID to enable a competitive manufacturing process in a Lean 4.0 environment. Furthermore, a MBID via OCR model is proposed to extract the printed identification number of packages from images captured by a fixed camera in an industrial environment. The method considers different digital image processing techniques to deal with the significant lighting and printing variation observed, followed by a segmentation process that extracts and aligns the characters. The proposed system utilized an approach to treating lighting variations in images, covering low contrast, distorted, darker, and brighter images. Experiments were carried out on a data set consisting of 200 images and achieved an overall detection accuracy of 95% with a very low Character Error Rate (CER) value of 0.0041, clearly supporting the validity and effectiveness of the proposed method.

Suggested Citation

  • Mohammad Shahin & F. Frank Chen & Ali Hosseinzadeh, 2024. "Machine-based identification system via optical character recognition," Flexible Services and Manufacturing Journal, Springer, vol. 36(2), pages 453-480, June.
    • Handle: RePEc:spr:flsman:v:36:y:2024:i:2:d:10.1007_s10696-023-09497-8
    DOI: 10.1007/s10696-023-09497-8
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    References listed on IDEAS

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    1. Thomas Hegghammer, 2022. "OCR with Tesseract, Amazon Textract, and Google Document AI: a benchmarking experiment," Journal of Computational Social Science, Springer, vol. 5(1), pages 861-882, May.
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