The recognition of vessel names (license plates) plays a crucial role in waterway transportation systems. Addressing the challenge of identifying vessel names in inland waterways, where targets are relatively small and vessels are observed at significant angular inclinations on both sides of the waterway, we propose an automatic ship name identification (ASNI) framework based on the differentiable binarization (DB) natural scene text detection algorithm and the convolutional recurrent neural network (CRNN) text recognition algorithm. ASNI comprises three main components: ship name detection, text image correction, and recognition. The text image correction component consists of a ship name correction module and a super-resolution reconstruction module. Firstly, the framework utilizes the DB algorithm to perform adaptive scale fusion processing on the candidate regions of vessel names in images, generating feature maps. Feature mapping is used to predict and generate binary images to identify connected regions, thereby obtaining regions of interest (ROI) containing vessel names. Subsequently, after ship name detection, a ship name correction module is introduced to rectify irregular text within the ROI using perspective transformation. Furthermore, a super-resolution reconstruction module is designed to enhance the resolution of the corrected vessel name images. Finally, the CRNN algorithm is employed to recognize vessel names within the corrected text images in the ROI, yielding the ultimate results. Through training and testing on the ship license plate (SLP) dataset specific to inland waterways, experimental results demonstrate that the ASNI framework achieves an average accuracy of 87.50% in vessel recognition, representing a 3.12% improvement over the baseline framework. The framework presented in this paper effectively addresses issues related to low resolution and angular inclinations leading to inaccurate vessel recognition. Compared to the baseline framework, ASNI exhibits superior recognition performance.