Exploring Out-of-Distribution Scene Text Recognition for Driving Scenes with Hybrid Test-Time Adaptation

Scene Text Recognition (STR) in dynamic driving scenes is important for recognizing real-world kilometer marker to facilitate the scheduling and operation of industrial scenes. For example, the location information of the train affects the safe and reliable operation of the transportation, which can be effectively determined by identifying the kilometer markers with STR technology. However, most of the existing STR models make the independent and identically distributed (i.i.d) assumption that all the training data and test data are drawn from the same data distribution. Although satisfactory performance is achieved under i.i.d assumption, existing STR models remain notoriously weak at generalization on out-of-distribution (o.o.d) data, making a system unreliable and unsafe. To validate this phenomenon, we attempt to propose a new hybrid test-time adaptation (HTTA) to improve the performance of an STR model on o.o.d test data. Previously, test-time adaptation methods are targeted at classification models and do not consider the multi-step reasoning characteristic of sequence learning tasks. In HTTA, we deploy multiple semantically-reserved image augmentation and design a semantically-consistent auxiliary task to present a continual adaptation. Additionally, we construct a new Real-world Subway Kilometer Marker (RSKM) dataset for an out-of-distribution STR practice under dynamic driving scenes. We conduct extensive experiments on RSKM by embedding our HTTA into multiple classical STR methods to show the effectiveness. The experiment results show that our semantically-consistent augmentation and HTTA significantly improve the generalization performance on o.o.d STR practice.