论文标题
通过字符锚池在场景识别中弹性功能聚集的灵活功能聚集的新观点
A New Perspective for Flexible Feature Gathering in Scene Text Recognition Via Character Anchor Pooling
论文作者
论文摘要
Irregular scene text recognition has attracted much attention from the research community, mainly due to the complexity of shapes of text in natural scene. 但是，最近的方法要么依赖于形状敏感的模块，例如边界框回归或丢弃序列学习。 To tackle these issues, we propose a pair of coupling modules, termed as Character Anchoring Module (CAM) and Anchor Pooling Module (APM), to extract high-level semantics from two-dimensional space to form feature sequences. 提出的CAM通过单独锚定字符来以一种不敏感的方式定位文本。 然后，APM插入式和聚集器沿着角色锚点灵活地特征，从而实现序列学习。 互补模块实现了空间信息和序列学习的谐波统一。 With the proposed modules, our recognition system surpasses previous state-of-the-art scores on irregular and perspective text datasets, including, ICDAR 2015, CUTE, and Total-Text, while paralleling state-of-the-art performance on regular text datasets.
Irregular scene text recognition has attracted much attention from the research community, mainly due to the complexity of shapes of text in natural scene. However, recent methods either rely on shape-sensitive modules such as bounding box regression, or discard sequence learning. To tackle these issues, we propose a pair of coupling modules, termed as Character Anchoring Module (CAM) and Anchor Pooling Module (APM), to extract high-level semantics from two-dimensional space to form feature sequences. The proposed CAM localizes the text in a shape-insensitive way by design by anchoring characters individually. APM then interpolates and gathers features flexibly along the character anchors which enables sequence learning. The complementary modules realize a harmonic unification of spatial information and sequence learning. With the proposed modules, our recognition system surpasses previous state-of-the-art scores on irregular and perspective text datasets, including, ICDAR 2015, CUTE, and Total-Text, while paralleling state-of-the-art performance on regular text datasets.
