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Automating Code Recognition for Cargo Containers

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Title: Automating Code Recognition for Cargo Containers
Authors: José Santos, Daniel Canedo, António J. R. Neves
Source: Electronics ; Volume 14 ; Issue 22 ; Pages: 4437
Publisher Information: Multidisciplinary Digital Publishing Institute
Publication Year: 2025
Collection: MDPI Open Access Publishing
Subject Terms: Optical Character Recognition (OCR), container code recognition, port operations, YOLOv7, TrOCR
Description: 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.
Document Type: text
File Description: application/pdf
Language: English
Relation: Computer Science & Engineering; https://dx.doi.org/10.3390/electronics14224437
DOI: 10.3390/electronics14224437
Availability: https://doi.org/10.3390/electronics14224437
Rights: https://creativecommons.org/licenses/by/4.0/
Accession Number: edsbas.ABD9196C
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  Data: Automating Code Recognition for Cargo Containers
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  Data: <searchLink fieldCode="AR" term="%22José+Santos%22">José Santos</searchLink><br /><searchLink fieldCode="AR" term="%22Daniel+Canedo%22">Daniel Canedo</searchLink><br /><searchLink fieldCode="AR" term="%22António+J%2E+R%2E+Neves%22">António J. R. Neves</searchLink>
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  Data: Electronics ; Volume 14 ; Issue 22 ; Pages: 4437
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  Data: Multidisciplinary Digital Publishing Institute
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  Data: 2025
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  Data: <searchLink fieldCode="DE" term="%22Optical+Character+Recognition+%28OCR%29%22">Optical Character Recognition (OCR)</searchLink><br /><searchLink fieldCode="DE" term="%22container+code+recognition%22">container code recognition</searchLink><br /><searchLink fieldCode="DE" term="%22port+operations%22">port operations</searchLink><br /><searchLink fieldCode="DE" term="%22YOLOv7%22">YOLOv7</searchLink><br /><searchLink fieldCode="DE" term="%22TrOCR%22">TrOCR</searchLink>
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  Label: Description
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  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.
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