UNSUPERVISED DOMAIN ADAPTATION WITH IMBALANCED CHARACTER DISTRIBUTION FOR SCENE TEXT RECOGNITION

Recent deep learning based methods have demonstrated promising results in scene text recognition. One of the major difficulty is the lack of manually annotated data. Synthetic data are then used to eliminate the requirement for human annotation. However, the domain gap between synthetic and real-world data remains a challenging issue. To bridge the gap, unsupervised domain adaptation (UDA) was introduced to transfer knowledge from a labeled source domain to a target domain. In this work, we introduce an unsupervised domain adaptation method based on a sequence-to-sequence attention model. We take into account imbalanced distribution of characters to optimize the adaptation process. We propose to use focal loss as the classification loss for the labeled source domain and focal entropy as the entropy loss for the unlabeled target domain. Our proposed method, named ICD-DA, outperforms other UDA methods on official benchmarks.