Open-set Text Recognition via Character-Context Decoupling

• URL: http://arxiv.org/abs/2204.05535v1
• Date: Tue, 12 Apr 2022 05:43:46 GMT
• Title: Open-set Text Recognition via Character-Context Decoupling
• Authors: Chang Liu, Chun Yang, Xu-Cheng Yin
• Abstract summary: 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. A Character-Context Decoupling framework is proposed to alleviate this problem by separating contextual information and character-visual information.
• Score: 16.2819099852748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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