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ProtoUDA: Prototype-Based Unsupervised Adaptation for Cross-Domain Text Recognition
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AI
DOI：10.1109/TKDE.2023.3344761.png)
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Abstract
En 中文
Text recognition reads from real scene text or handwritten text, facilitating many real-world applications such as driverless cars, visual Q&A, and image-based machine translation. Although impressive results have been achieved in single-domain text recognition, it still suffers from great challenges in cross-domain due to the domain gaps among the synthetic text, the real scene text, and the handwritten text. Existing standard unsupervised domain adaptation (UDA) methods struggle to solve the text recognition task since they view a domain or a text image (containing a character sequence) as a whole, ignoring the subunits that make up the sequence. In the paper, we present a Prototyped-based Unsupervised Domain Adaptation method for text recognition (ProtoUDA), where the class prototypes are computed from the source domain, target domain, and the mixed (source-target) domain, respectively. Technically, ProtoUDA initially extracts pseudo-labeled character features under word-level supervised information. Further, based on these character features, we propose two parallel and complementary modules to perform class-level and instance-level alignment, which explicitly transfer the knowledge learned in the source domain to the target domain. Among them, class-level alignment is to close the distance between the similar source prototypes and target prototypes. The instance-level alignment is based on contrastive learning, making the character instances of the mixed domain close to the corresponding class mixed prototype while staying away from other class mixed prototypes. To our knowledge, we are the first to adopt contrastive learning in UDA-based text recognition tasks. Extensive experiments on several benchmark datasets show the superiority of our method over state-of-the-art methods.
Keywords:
Text recognition
Prototypes
Feature extraction
Task analysis
Visualization
Decoding
Adaptation models
Unsupervised learning
prototype
text recognition
contrastive learning
domain adaptation
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