The Transformer-based encoder-decoder framework is becoming popular in scene
text recognition, largely because it naturally integrates recognition clues
from both visual and semantic domains. However, recent studies show that the
two kinds of clues are not always well registered and therefore, feature and
character might be misaligned in the difficult text (e.g., with rare shapes).
As a result, constraints such as character position are introduced to alleviate
this problem. Despite certain success, a content-free positional embedding
hardly associates stably with meaningful local image regions. In this paper, we
propose a novel module called Multi-Domain Character Distance Perception
(MDCDP) to establish a visual and semantic related positional encoding. MDCDP
uses positional embedding to query both visual and semantic features following
the attention mechanism. The two kinds of constrained features are then fused
to produce a reinforced feature, generating a content-aware embedding that well
perceives spacing variations and semantic affinities among characters, i.e.,
multi-domain character distance. We develop a novel network named CDistNet that
stacks multiple MDCDPs to guide a gradually precise distance modeling. Thus,
the feature-character alignment is well built even various recognition
difficulties presented. We create two series of augmented datasets with
increasing recognition difficulties and apply CDistNet to both them and six
public benchmarks. The experiments demonstrate that CDistNet outperforms recent
popular methods by large margins in challenging recognition scenarios. It also
achieves state-of-the-art accuracy on standard benchmarks. In addition, the
visualization shows that CDistNet achieves proper information utilization in
both visual and semantic domains. Our code is given in
this https URL

本文提出了一种名为 MDCDP 的模块，通过引入注意机制，利用位置编码查询视觉和语义特征，融合两种受约束特征以产生加强的特征，生成良好的内容感知嵌入，能够很好地感知字符之间的间距变化和语义亲和力。通过将多个 MDCDP 堆叠以引导逐渐准确的距离建模，成功构建了特征 - 字符对齐，解决了在文本识别中出现的各种困难问题。我们在两个增强数据集和六个公共基准上进行了实验，结果表明 CDistNet 在具有挑战性的识别场景中大幅优于最近的热门方法，也在标准基准上取得了最新的准确性。此外，可视化结果表明 CDistNet 在视觉和语义领域都实现了合适的信息利用。