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Towards end-to-end container code recognition

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Abstract

Container code recognition can improve the efficiency and economy of the management system in the port. However, the task is different and complex due to the degradation of image quality caused by uneven illumination, background variation, smear, inaccurate character extraction, and so on. Current processing methods on container images usually provide the framework or modules on specific tasks, such as region detection and character classification, which are hard to implement or to be combined into a whole process. In this paper, we propose a fast end-to-end method of automatic recognition of container code that fills the gap by locating the region and detecting characters as well as making the classification. This allows the three tasks to work collaboratively by pipeline, which is critical to identify the container code. For evaluation, we collect around six thousand container images, including all kinds of circumstances from the local port. Compared with a few other methods and two-step approaches consisting of state-of-the-art character detector and character classifier, our system achieves some competitive results. Finally, the proposed system is verified on this dataset and the overall accuracy reaches 97.30%.

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Notes

  1. Nanjing Port is located in Nanjing, Jiangsu Province, China, and is the largest inland port in the world (depending on how you classify the ports in the Yangtze Delta), with throughput reaching 191 million tons of cargo in 2012.

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Acknowledgements

We specially thanks the Nanjing Port for providing us the datasets and technique support. This work is supported in part by the Key Program of the National Natural Science Foundation of China (61932013), the Natural Science Foundation of Jiangsu Province of China (BK20200739), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (20KJB520003) and the Research Foundation of Jiangsu for “333 high level talents training project” (BRA2020065). This work is also supported by the China Postdoctoral Science Foundation (2021M691655) and the Postdoctoral Science Foundation of Jiangsu Province of China (2021K172B). This article is also sponsored by NUPTSF (NY219149, NY220189). Yanchao Li is also supported by Henan Key Laboratory of Food Safety Data Intelligence, ZZULI (KF2020YB01).

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Authors and Affiliations

  1. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

    Yanchao Li & Guangwei Gao

  2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Hao Li

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  1. Yanchao Li
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  2. Hao Li
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  3. Guangwei Gao
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Correspondence to Yanchao Li.

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Li, Y., Li, H. & Gao, G. Towards end-to-end container code recognition. Multimed Tools Appl 81, 15901–15918 (2022). https://doi.org/10.1007/s11042-022-12477-z

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