Abstract
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.
Abstract (translated)
在本文中，我们提出了一种通过判断图像和文本是否匹配来提高场景文本识别任务准确性的方法。与之前的研究不同，我们的方法考虑了模型的误识别结果，以了解其错误趋势，从而改善了文本识别流程。通过为模型提供关于其可能误识别的图像和文本之间的明确反馈，这种方法提高了文本识别的准确性。在公开数据集上进行的实验结果表明，与基线和最先进的方法相比，我们提出的方法在场景文本识别中表现出色。
URL
https://arxiv.org/abs/2404.05967
