Linguistic knowledge has brought great benefits to scene text recognition by providing semantics to refine character sequences. However, since linguistic knowledge has been applied individually on the output sequence, previous methods have not fully utilized the semantics to understand visual clues for text recognition. This paper introduces a novel method, called Multi-modAl Text Recognition Network (MATRN), that enables interactions between visual and semantic features for better recognition performances. Specifically, MATRN identifies visual and semantic feature pairs and encodes spatial information into semantic features. Based on the spatial encoding, visual and semantic features are enhanced by referring to related features in the other modality. Furthermore, MATRN stimulates combining semantic features into visual features by hiding visual clues related to the character in the training phase. Our experiments demonstrate that MATRN achieves state-of-the-art performances on seven benchmarks with large margins, while naive combinations of two modalities show marginal improvements. Further ablative studies prove the effectiveness of our proposed components. Our implementation will be publicly available.

本篇论文介绍了一种名为MATRN（Multi-modAl Text Recognition Network）的新方法，通过促进视觉和语义特征之间的互动，提高了文字识别的性能，并证明其在7项基准测试上取得了最先进的表现。

多模态文本识别网络：视觉和语义特征之间的交互增强