Open-Vocabulary Scene Text Recognition via Pseudo-Image Labeling and
  Margin Loss
Xuhua Ren Hengcan Shi Jin Li
Scene text recognition is an important and challenging task in computer
vision. However, most prior works focus on recognizing pre-defined words, while
there are various out-of-vocabulary (OOV) words in real-world applications.
  In this paper, we propose a novel open-vocabulary text recognition framework,
Pseudo-OCR, to recognize OOV words. The key challenge in this task is the lack
of OOV training data. To solve this problem, we first propose a pseudo label
generation module that leverages character detection and image inpainting to
produce substantial pseudo OOV training data from real-world images. Unlike
previous synthetic data, our pseudo OOV data contains real characters and
backgrounds to simulate real-world applications. Secondly, to reduce noises in
pseudo data, we present a semantic checking mechanism to filter semantically
meaningful data. Thirdly, we introduce a quality-aware margin loss to boost the
training with pseudo data. Our loss includes a margin-based part to enhance the
classification ability, and a quality-aware part to penalize low-quality
samples in both real and pseudo data.
  Extensive experiments demonstrate that our approach outperforms the
state-of-the-art on eight datasets and achieves the first rank in the ICDAR2022
challenge.