Pushing the Performance Limit of Scene Text Recognizer without Human Annotation
This work addresses the domain gap issue in STR for applications requiring text recognition, but it is incremental as it builds on existing semi-supervised methods.
The paper tackles the problem of scene text recognition (STR) by proposing a semi-supervised framework that uses synthetic and real unlabeled images to avoid human annotation, achieving new state-of-the-art results on standard benchmarks.
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.