Abstract
We introduce SELECT (Scene tExt Label Errors deteCTion), a novel approach that leverages multi-modal training to detect label errors in real-world scene text datasets. Utilizing an image-text encoder and a character-level tokenizer, SELECT addresses the issues of variable-length sequence labels, label sequence misalignment, and character-level errors, outperforming existing methods in accuracy and practical utility. In addition, we introduce Similarity-based Sequence Label Corruption (SSLC), a process that intentionally introduces errors into the training labels to mimic real-world error scenarios during training. SSLC not only can cause a change in the sequence length but also takes into account the visual similarity between characters during corruption. Our method is the first to detect label errors in real-world scene text datasets successfully accounting for variable-length labels. Experimental results demonstrate the effectiveness of SELECT in detecting label errors and improving STR accuracy on real-world text datasets, showcasing its practical utility.
Abstract (translated)
我们引入了SELECT（Scene tExt Label Errors deteCTion），这是一种利用多模态训练来检测现实世界场景文本数据集中标签错误的创新方法。通过使用图像-文本编码器和字符级分词器，SELECT 解决了可变长度序列标签、标签序列错位以及字符级别错误的问题，并在准确性和实用性方面优于现有方法。此外，我们还引入了一种基于相似性的序列标签腐败（SSLC）过程，在训练过程中故意向训练标签中引入错误以模拟现实世界中的错误场景。SSLC 不仅可以改变序列长度，还可以在腐败过程中考虑字符之间的视觉相似性。我们的方法是首次成功地检测到真实世界的场景文本数据集中可变长度标签的标签错误的方法。实验结果证明了SELECT 在检测标签错误和提高实际文本数据集上的STR准确性方面的有效性，展示了其实用价值。
URL
https://arxiv.org/abs/2512.14050
