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Automating Code Recognition for Cargo Containers.

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Title: Automating Code Recognition for Cargo Containers.
Authors: Santos, José, Canedo, Daniel, Neves, António J. R.
Source: Electronics (2079-9292); Nov2025, Vol. 14 Issue 22, p4437, 18p
Subject Terms: OPTICAL character recognition, SHIPPING containers, IMAGE processing software, MARITIME shipping, PORTS (Electronic computer system), OPERATIONS management
Abstract: Maritime transport plays a pivotal role in global trade, where efficiency and accuracy in port operations are crucial. Among the various tasks carried out in ports, container code recognition is essential for tracking and handling cargo. Manual inspections of container codes are becoming increasingly impractical, as they induce delays and raise the risk of human error. To address these issues, this work proposes a hybrid Optical Character Recognition system that integrates YOLOv7 for text detection with the transformer-based TrOCR for recognition of the container codes, enabling accurate and efficient automated recognition. This design addresses the real-world challenges, such as varying light, distortions, and multi-orientation of container codes. To evaluate the system, we conducted a comprehensive evaluation on datasets that simulate the conditions found in port environments. The results demonstrate that the proposed hybrid model delivers significant improvements in detection and recognition accuracy and robustness compared to traditional OCR methods. In particular, the reliability in recognizing multi-oriented codes marks a notable advancement compared to existing solutions. Overall, this study presents an approach to automating container code recognition, contributing to the efficiency and modernization of port operations, with the potential to streamline port operations, reduce human error, and enhance the overall logistics workflow. [ABSTRACT FROM AUTHOR]
Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
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  Data: Automating Code Recognition for Cargo Containers.
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  Data: <searchLink fieldCode="AR" term="%22Santos%2C+José%22">Santos, José</searchLink><br /><searchLink fieldCode="AR" term="%22Canedo%2C+Daniel%22">Canedo, Daniel</searchLink><br /><searchLink fieldCode="AR" term="%22Neves%2C+António+J%2E+R%2E%22">Neves, António J. R.</searchLink>
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  Data: Electronics (2079-9292); Nov2025, Vol. 14 Issue 22, p4437, 18p
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  Data: <searchLink fieldCode="DE" term="%22OPTICAL+character+recognition%22">OPTICAL character recognition</searchLink><br /><searchLink fieldCode="DE" term="%22SHIPPING+containers%22">SHIPPING containers</searchLink><br /><searchLink fieldCode="DE" term="%22IMAGE+processing+software%22">IMAGE processing software</searchLink><br /><searchLink fieldCode="DE" term="%22MARITIME+shipping%22">MARITIME shipping</searchLink><br /><searchLink fieldCode="DE" term="%22PORTS+%28Electronic+computer+system%29%22">PORTS (Electronic computer system)</searchLink><br /><searchLink fieldCode="DE" term="%22OPERATIONS+management%22">OPERATIONS management</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Maritime transport plays a pivotal role in global trade, where efficiency and accuracy in port operations are crucial. Among the various tasks carried out in ports, container code recognition is essential for tracking and handling cargo. Manual inspections of container codes are becoming increasingly impractical, as they induce delays and raise the risk of human error. To address these issues, this work proposes a hybrid Optical Character Recognition system that integrates YOLOv7 for text detection with the transformer-based TrOCR for recognition of the container codes, enabling accurate and efficient automated recognition. This design addresses the real-world challenges, such as varying light, distortions, and multi-orientation of container codes. To evaluate the system, we conducted a comprehensive evaluation on datasets that simulate the conditions found in port environments. The results demonstrate that the proposed hybrid model delivers significant improvements in detection and recognition accuracy and robustness compared to traditional OCR methods. In particular, the reliability in recognizing multi-oriented codes marks a notable advancement compared to existing solutions. Overall, this study presents an approach to automating container code recognition, contributing to the efficiency and modernization of port operations, with the potential to streamline port operations, reduce human error, and enhance the overall logistics workflow. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.3390/electronics14224437
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      – Code: eng
        Text: English
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        PageCount: 18
        StartPage: 4437
    Subjects:
      – SubjectFull: OPTICAL character recognition
        Type: general
      – SubjectFull: SHIPPING containers
        Type: general
      – SubjectFull: IMAGE processing software
        Type: general
      – SubjectFull: MARITIME shipping
        Type: general
      – SubjectFull: PORTS (Electronic computer system)
        Type: general
      – SubjectFull: OPERATIONS management
        Type: general
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      – TitleFull: Automating Code Recognition for Cargo Containers.
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            NameFull: Santos, José
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            NameFull: Canedo, Daniel
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            – D: 15
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
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