IDEAS home Printed from https://ideas.repec.org/a/spr/flsman/v36y2024i2d10.1007_s10696-023-09497-8.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10696-023-09497-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10696-023-09497-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Frank Chen & Zvi Drezner & Jennifer K. Ryan & David Simchi-Levi, 2000. "Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information," Management Science, INFORMS, vol. 46(3), pages 436-443, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pastore, Erica & Alfieri, Arianna & Zotteri, Giulio, 2019. "An empirical investigation on the antecedents of the bullwhip effect: Evidence from the spare parts industry," International Journal of Production Economics, Elsevier, vol. 209(C), pages 121-133.
    2. Wang, Zhaodong & Wang, Xin & Ouyang, Yanfeng, 2015. "Bounded growth of the bullwhip effect under a class of nonlinear ordering policies," European Journal of Operational Research, Elsevier, vol. 247(1), pages 72-82.
    3. Ma, Yungao & Wang, Nengmin & He, Zhengwen & Lu, Jizhou & Liang, Huigang, 2015. "Analysis of the bullwhip effect in two parallel supply chains with interacting price-sensitive demands," European Journal of Operational Research, Elsevier, vol. 243(3), pages 815-825.
    4. Yongchang Wei & Fangyu Chen & Feng Xiong, 2018. "Dynamic Complexities in a Supply Chain System with Lateral Transshipments," Complexity, Hindawi, vol. 2018, pages 1-15, June.
    5. Ananth V. Iyer & Apurva Jain, 2003. "The Logistics Impact of a Mixture of Order-Streams in a Manufacturer-Retailer System," Management Science, INFORMS, vol. 49(7), pages 890-906, July.
    6. Li Chen & Hau L. Lee, 2012. "Bullwhip Effect Measurement and Its Implications," Operations Research, INFORMS, vol. 60(4), pages 771-784, August.
    7. Kamath, Narasimha B. & Roy, Rahul, 2007. "Capacity augmentation of a supply chain for a short lifecycle product: A system dynamics framework," European Journal of Operational Research, Elsevier, vol. 179(2), pages 334-351, June.
    8. Sajjad Aslani Khiavi & Hamid Khaloozadeh & Fahimeh Soltanian, 2021. "Suboptimal sliding manifold For nonlinear supply chain with time delay," Journal of Combinatorial Optimization, Springer, vol. 42(1), pages 151-173, July.
    9. Zhang, Xiaolong & Burke, Gerard J., 2011. "Analysis of compound bullwhip effect causes," European Journal of Operational Research, Elsevier, vol. 210(3), pages 514-526, May.
    10. Gaalman, Gerard & Disney, Stephen M., 2009. "On bullwhip in a family of order-up-to policies with ARMA(2,2) demand and arbitrary lead-times," International Journal of Production Economics, Elsevier, vol. 121(2), pages 454-463, October.
    11. Zhao, Xiande & Xie, Jinxing & Leung, Janny, 2002. "The impact of forecasting model selection on the value of information sharing in a supply chain," European Journal of Operational Research, Elsevier, vol. 142(2), pages 321-344, October.
    12. Lee, Jongkuk & Palekar, Udatta S. & Qualls, William, 2011. "Supply chain efficiency and security: Coordination for collaborative investment in technology," European Journal of Operational Research, Elsevier, vol. 210(3), pages 568-578, May.
    13. Jiewu Leng & Pingyu Jiang, 2019. "Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 979-994, March.
    14. Hancock, Mary Everett & Mora, Jesse, 2023. "The Impact of COVID-19 on Chinese trade and production: An empirical analysis of processing trade with Japan and the US," Journal of Asian Economics, Elsevier, vol. 86(C).
    15. Yue, Xiaohang & Liu, John, 2006. "Demand forecast sharing in a dual-channel supply chain," European Journal of Operational Research, Elsevier, vol. 174(1), pages 646-667, October.
    16. Cannella, Salvatore & Bruccoleri, Manfredi & Framinan, Jose M., 2016. "Closed-loop supply chains: What reverse logistics factors influence performance?," International Journal of Production Economics, Elsevier, vol. 175(C), pages 35-49.
    17. Joao Montez & Nicolas Schutz, 2021. "All-Pay Oligopolies: Price Competition with Unobservable Inventory Choices [Extremal Equilibria of Oligopolistic Supergames]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(5), pages 2407-2438.
    18. Jaksic, Marko & Rusjan, Borut, 2008. "The effect of replenishment policies on the bullwhip effect: A transfer function approach," European Journal of Operational Research, Elsevier, vol. 184(3), pages 946-961, February.
    19. Ranveer Singh Rana & Dinesh Kumar & Kanika Prasad & K. Mathiyazhagan, 2024. "Mitigating the impact of demand disruption on perishable inventory in a two-warehouse system," Operations Management Research, Springer, vol. 17(2), pages 469-504, June.
    20. Rafael Diaz, 2016. "Using dynamic demand information and zoning for the storage of non-uniform density stock keeping units," International Journal of Production Research, Taylor & Francis Journals, vol. 54(8), pages 2487-2498, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:flsman:v:36:y:2024:i:2:d:10.1007_s10696-023-09497-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.