  Scene text recognition (STR) task has a common practice: All state-of-the-art
STR models are trained on large synthetic data. In contrast to this practice,
training STR models only on fewer real labels (STR with fewer labels) is
important when we have to train STR models without synthetic data: for
handwritten or artistic texts that are difficult to generate synthetically and
for languages other than English for which we do not always have synthetic
data. However, there has been implicit common knowledge that training STR
models on real data is nearly impossible because real data is insufficient. We
consider that this common knowledge has obstructed the study of STR with fewer
labels. In this work, we would like to reactivate STR with fewer labels by
disproving the common knowledge. We consolidate recently accumulated public
real data and show that we can train STR models satisfactorily only with real
labeled data. Subsequently, we find simple data augmentation to fully exploit
real data. Furthermore, we improve the models by collecting unlabeled data and
introducing semi- and self-supervised methods. As a result, we obtain a
competitive model to state-of-the-art methods. To the best of our knowledge,
this is the first study that 1) shows sufficient performance by only using real
labels and 2) introduces semi- and self-supervised methods into STR with fewer
labels. Our code and data are available:
https://github.com/ku21fan/STR-Fewer-Labels
