ContraText-DETR: Boosting Industrial Scene Text Detection Based on Contrastive Learning and Synthetic Low-Contrast Text

The scene text detection in industrial environments is challenging due to low contrast, corrosion, and glare on metallic surfaces, which affect the detection accuracy. Furthermore, symbols such as engraved logos resemble text, leading to misclassification and increased false positive rates. In this study, we propose a framework that applies synthetic data augmentation to handle low-contrast conditions and employs contrastive learning to enhance text–symbol differentiation. This framework improves robustness and generalization by training the model on diverse low-contrast scenarios. Furthermore, it reduces misclassification by using false positives as negative samples, enhancing the separation between text and symbols. Since the proposed method is adopted only during the training phase, it avoids computational overhead during inference. Moreover, it does not rely on modifications to the architecture, allowing flexible integration into existing text detectors. In addition, we observed ambiguities in the annotation criteria of the MPSC dataset, particularly in distinguishing individual text instances and labeling their ground-truth bounding boxes. To resolve this, we reannotate the dataset by defining a consistent standard for text instance labeling. Our experimental results demonstrate that the proposed model achieves state-of-the-art performance on the MPSC dataset, validating its applicability to industrial text detection tasks.