The open-set text recognition task is an emerging challenge that requires an extra capability to cognize novel characters during evaluation. We argue that a major cause of the limited performance for current methods is the confounding effect of contextual information over the visual information of individual characters. Under open-set scenarios, the intractable bias in contextual information can be passed down to visual information, consequently impairing the classification performance. In this paper, a Character-Context Decoupling framework is proposed to alleviate this problem by separating contextual information and character-visual information. Contextual information can be decomposed into temporal information and linguistic information. Here, temporal information that models character order and word length is isolated with a detached temporal attention module. Linguistic information that models n-gram and other linguistic statistics is separated with a decoupled context anchor mechanism. A variety of quantitative and qualitative experiments show that our method achieves promising performance on open-set, zero-shot, and close-set text recognition datasets.

本文提出了一个字符-上下文解耦模型，该模型通过分离上下文信息和字符视觉信息来缓解上下文信息对识别性能的影响，通过定时注意力模块隔离时间信息和语义锚定机制分离语言信息，本方法在开放式、零样本和闭合式文本识别数据集上取得了有希望的表现。

基于字符—上下文解耦的开放式文本识别