  Incorporating linguistic knowledge can improve scene text recognition, but it
is questionable whether the same holds for scene text spotting, which typically
involves text detection and recognition. This paper proposes a method that
leverages linguistic knowledge from a large text corpus to replace the
traditional one-hot encoding used in auto-regressive scene text spotting and
recognition models. This allows the model to capture the relationship between
characters in the same word. Additionally, we introduce a technique to generate
text distributions that align well with scene text datasets, removing the need
for in-domain fine-tuning. As a result, the newly created text distributions
are more informative than pure one-hot encoding, leading to improved spotting
and recognition performance. Our method is simple and efficient, and it can
easily be integrated into existing auto-regressive-based approaches.
Experimental results show that our method not only improves recognition
accuracy but also enables more accurate localization of words. It significantly
improves both state-of-the-art scene text spotting and recognition pipelines,
achieving state-of-the-art results on several benchmarks.
