With the widespread use of containers, the demand for Container Marking Detection and Recognition (CMDR) is gradually increasing. The use of deep learning algorithms can greatly improve the efficiency of marking detection and recognition. However, there is still a lack of research on CMDR in both academia and industry, resulting in the current task being completed manually and inefficiently. In this paper, we probe into the importance of data-driven and task paradigms for CMDR tasks. Firstly, we constructed an open large scale container surface marking text dataset called ContainerText. This dataset consists of 12 k high-resolution images and provides two types of annotation information: bounding box used for detection and text for recognition tasks. In addition, we also propose an efficient semi-automatic annotation method based on deep learning, which reduces the cost of manual annotation. Subsequently, we have innovatively proposed a CMDR method combining Scene Text Recognition (STR) with CMDR tasks. The method based on STR can locate and recognize container marking from a fine-grained level. We conducted a comprehensive series of experiments on the ContainerText dataset using state-of-the-art (SOTA) scene text detection and scene text recognition models. The experimental results demonstrate that the CMDR method, based on STR, exhibits exceptional adaptability and feasibility. All experimental results obtained from the ContainerText dataset will act as performance benchmarks for future researchers. Finally, an automated Container Marking Image Acquisition Mechanism (CMIAM) are construucted, which can effectively avoid complex lighting in the workshop environment and achieve high-quality and automated image acquisition. We have conducted extensive experiments to measure the distance, resolution, and field of view required for clearly capturing container markings. Our research providing reference for future CMDR research from task solution and hardware selection.