  In recent years, deep learning-based methods have shown promising results in
computer vision area. However, a common deep learning model requires a large
amount of labeled data, which is labor-intensive to collect and label. What's
more, the model can be ruined due to the domain shift between training data and
testing data. Text recognition is a broadly studied field in computer vision
and suffers from the same problems noted above due to the diversity of fonts
and complicated backgrounds. In this paper, we focus on the text recognition
problem and mainly make three contributions toward these problems. First, we
collect a multi-source domain adaptation dataset for text recognition,
including five different domains with over five million images, which is the
first multi-domain text recognition dataset to our best knowledge. Secondly, we
propose a new method called Meta Self-Learning, which combines the
self-learning method with the meta-learning paradigm and achieves a better
recognition result under the scene of multi-domain adaptation. Thirdly,
extensive experiments are conducted on the dataset to provide a benchmark and
also show the effectiveness of our method. The code of our work and dataset are
available soon at https://bupt-ai-cz.github.io/Meta-SelfLearning/.
