A Vertical Text Spotting Model for Trailer and Container Codes

Accurately detecting and recognizing vertical codes of trailers and containers plays a crucial role in logistic management during freight transportation, especially in the absence of horizontal codes. Existing research mainly focuses on developing text spotting models for texts in the direction from left to right, with or without rotation, in curved or straight lines. Vertical code spotting has not been fully explored yet. Thus, in this study, we propose a vertical text spotting model for vertical trailer and container codes. Our model adopts a multistage detector for better localization performance. The regional text features extracted by backbone network are pooled vertically with vertical RoIAlign to conform with the code direction for the convenience of recognition. In the recognition part, our model integrates two recognition pipelines that take cropped images and extracted features. The recognition results from two pipelines are selected by the proposed decision strategy considering uncertainties and probabilities. Experimental results show that our proposed model outperforms other state-of-the-art text spotting models in detecting and recognizing vertical codes.