Combining Autoregressive and Non-Autoregressive Models for Ship License Plate Recognition

Ship license plate recognition (SLPR) is a fundamental visual task in intelligent waterway transportation systems that aims to transcribe ship name text images into editable text strings. Previous works construct recognizers with complicated training procedures and additional corpora to boost performance, limiting their efficiency in practical application scenarios. This paper proposes a novel ship license plate recognizer by combining autoregressive (AR) and non-autoregressive (NAR) decoding mechanisms. By adaptively incorporating the dual-branch character representations, our method explicitly sidesteps the dependence on an external language model and is adapted to the weak semantic correlation characteristics of ship name text images. Furthermore, we introduce a confidence-based dynamic reweighting strategy that includes character-and instance-level granularities. This encourages the model to learn more from hard or challenging samples. Experimental results conducted on two SLPR benchmarks demonstrate the effectiveness of our method, showing that it is competitive and outperforms previous approaches. Our study comprehensively explores the relative importance of linguistic and visual cues in SLPR and demonstrates the advantages of the dynamically fused model with dual-branch autoregressive and non-autoregressive over single-branch recognition.