  Recent works in the text recognition area have pushed forward the recognition
results to the new horizons. But for a long time a lack of large human-labeled
natural text recognition datasets has been forcing researchers to use synthetic
data for training text recognition models. Even though synthetic datasets are
very large (MJSynth and SynthTest, two most famous synthetic datasets, have
several million images each), their diversity could be insufficient, compared
to natural datasets like ICDAR and others. Fortunately, the recently released
text-recognition annotation for OpenImages V5 dataset has comparable with
synthetic dataset number of instances and more diverse examples. We have used
this annotation with a Text Recognition head architecture from the Yet Another
Mask Text Spotter and got comparable to the SOTA results. On some datasets we
have even outperformed previous SOTA models. In this paper we also introduce a
text recognition model. The model's code is available.
