  Text recognition in the wild is a long-standing problem in computer vision.
Driven by end-to-end deep learning, recent studies suggest vision and language
processing are effective for scene text recognition. Yet, solving edit errors
such as add, delete, or replace is still the main challenge for existing
approaches. In fact, the content of the text and its audio are naturally
corresponding to each other, i.e., a single character error may result in a
clear different pronunciation. In this paper, we propose the AudioOCR, a simple
yet effective probabilistic audio decoder for mel spectrogram sequence
prediction to guide the scene text recognition, which only participates in the
training phase and brings no extra cost during the inference stage. The
underlying principle of AudioOCR can be easily applied to the existing
approaches. Experiments using 7 previous scene text recognition methods on 12
existing regular, irregular, and occluded benchmarks demonstrate our proposed
method can bring consistent improvement. More importantly, through our
experimentation, we show that AudioOCR possesses a generalizability that
extends to more challenging scenarios, including recognizing non-English text,
out-of-vocabulary words, and text with various accents. Code will be available
at https://github.com/wenwenyu/AudioOCR.
