Globalization has spurred the rapid expansion of international trade, which in turn demands higher efficiency and accuracy in logistics and shipping. Containers, central to global trade, rely on advanced recognition technology to significantly boost the efficiency of port operations. This review discusses the progression, key innovations, and hurdles in the smart recognition of container numbers, focusing on technologies like Radio Frequency Identification (RFID), video recognition, and Optical Character Recognition (OCR), which dominate the field. It explores methods such as image preprocessing, character segmentation, and feature extraction, highlighting the advantages and challenges of each. Deep learning approaches, especially those utilizing DBN and CNN frameworks, are particularly noted for their precise and robust feature analysis, despite facing issues with real-time processing and durability. Research is moving towards reducing reliance on extensive annotated datasets, enhancing the efficiency of these algorithms, and integrating them more effectively into logistics management systems.