Efficiently Leveraging Linguistic Priors for Scene Text Spotting
Nguyen Nguyen Yapeng Tian Chenliang Xu
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.