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.

翻译：本文提出一种通过判断图像与文本是否匹配来提升场景文本识别任务准确率的方法。先前研究主要聚焦于从输入图像生成识别结果，而我们的方法进一步利用模型的错误识别结果来理解其错误倾向，从而改进文本识别流程。该方法通过预测图像与文本之间的正确/错误匹配，为模型可能误识别的数据提供显式反馈，进而提升文本识别准确率。在公开数据集上的实验结果表明，我们提出的方法在场景文本识别任务中优于基线方法和当前最先进方法。