Scene text recognition (STR) attracts much attention over the years because
of its wide application. Most methods train STR model in a fully supervised
manner which requires large amounts of labeled data. Although synthetic data
contributes a lot to STR, it suffers from the real-tosynthetic domain gap that
restricts model performance. In this work, we aim to boost STR models by
leveraging both synthetic data and the numerous real unlabeled images,
exempting human annotation cost thoroughly. A robust consistency regularization
based semi-supervised framework is proposed for STR, which can effectively
solve the instability issue due to domain inconsistency between synthetic and
real images. A character-level consistency regularization is designed to
mitigate the misalignment between characters in sequence recognition. Extensive
experiments on standard text recognition benchmarks demonstrate the
effectiveness of the proposed method. It can steadily improve existing STR
models, and boost an STR model to achieve new state-of-the-art results. To our
best knowledge, this is the first consistency regularization based framework
that applies successfully to STR.

本文介绍了一种利用合成数据和大量真实未标记图像的半监督框架来提高场景文本识别模型性能的方法。该框架基于鲁棒的一致性正则化，有效地解决了合成和真实图像之间的域不一致性问题，并在多项实验中显示出稳定性和准确性，同时提高了场景文本识别模型达到了最新的最优结果，是首个成功应用一致性正则化框架于场景文本识别的方法。