Yong Hu
ABSTRACT
Scene text detection methods based on deep learning have achieved remarkable success. To address the laborious and time-consuming process of manually annotating datasets, a large amount of synthetic data has been created and utilized. However, due to the domain discrepancy between synthetic and real scene data, models trained on synthetic data may suffer from performance degradation when applied to real scenes. In order to tackle the domain shift issue between synthetic and real scene data, we propose the Orthogonal Feature Alignment Network (OFAN) specifically designed for text objects. OFAN incorporates an orthogonal feature enhancement module to strengthen the edge features of text instances, emphasizing the text objects, and employs adversarial training for text instance alignment across domains. Additionally, a multi-transform self-training mixture technique is utilized to further improve the detection performance of the model in the target domain, mitigating the adverse effects of false positives and false negatives. We extensively evaluate OFAN on four benchmark datasets, and the experimental results demonstrate the effectiveness of our approach.
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Index Terms
- Orthogonal Feature Alignment Network for Cross-Domain Text Detection
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