In order to tackle the challenges associated with small character regions, complex lighting variations, and poor image quality in the identification of slab numbers in steel continuous casting production lines, a two-stage algorithm is proposed for slab number detection and recognition, utilizing deep learning techniques. Firstly, datasets for slab number detection and recognition are prepared based on the collected slab images of a continuous casting production line. Secondly, in the slab number detection stage, an AD-PAN feature fusion structure based on the DBNet algorithm is designed to enhance the multi-scale feature fusion capability and expand the receptive field of the detection algorithm, thereby improving the localization accuracies of the slab numbers. Thirdly, in the slab number recognition stage, the proposed algorithm incorporates a SPIN correction network and a SVTR slab number recognition network for end-to-end training, enabling them to actively transform input brightness and improve color distortion among characters and between characters and backgrounds. Finally, comparative experiments are conducted on self-made datasets for slab number detection and recognition, and experimental results demonstrate that the algorithm proposed in this study is capable of effectively locating slab at different positions on the roller table and robustly recognizing slab numbers in complex backgrounds. The Hmean value for slab number detection is 97.92%, and the accuracy for slab number recognition is 97.33%, confirming the high accuracy of slab number detection and recognition achieved by the proposed algorithm.