Lifelong Scene Text Recognizer via Expert Modules

Scene text recognition (STR) has been actively studied in recent years, with a wide range of applications in autonomous driving, image retrieval and much more. However, when a pre-trained deep STR model learns a new task, its performance on previous tasks may drop dramatically, due to catastrophic forgetting in deep neural networks. A potential solution to combat the forgetting of prior knowledge is incremental learning (IL), which has shown its effectiveness and significant progress in image classification. Yet, exploiting IL in the context of STR has been barely visited, probably because the forgetting problem is even worse in STR. To address this issue, we propose the lifelong scene text recognizer (LSTR) that learns STR tasks incrementally while alleviating forgetting. Specifically, LSTR assigns each task a set of task-specific expert modules at different stages of an STR model, while other parameters are shared among tasks. These shared parameters are only learned in the first task and remain unchanged during subsequent learning to ensure that no learned knowledge is overlooked. Moreover, in real applications, there is no prior knowledge about which task an input image belongs to, making it impossible to precisely select the corresponding expert modules. To this end, we propose the incremental task prediction network (ITPN) to identify the most related task category by pulling the features of the same task closer and pushing those of different tasks farther apart. To validate the proposed method in our newly-introduced IL setting, we collected a large-scale dataset consisting of both real and synthetic multilingual STR data. Extensive experiments on this dataset clearly show the superiority of our LSTR over state-of-the-art IL methods.