  In this paper, we present a method for enhancing the accuracy of scene text
recognition tasks by judging whether the image and text match each other. While
previous studies focused on generating the recognition results from input
images, our approach also considers the model's misrecognition results to
understand its error tendencies, thus improving the text recognition pipeline.
This method boosts text recognition accuracy by providing explicit feedback on
the data that the model is likely to misrecognize by predicting correct or
incorrect between the image and text. The experimental results on publicly
available datasets demonstrate that our proposed method outperforms the
baseline and state-of-the-art methods in scene text recognition.
