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Robust Text Image Recognition via Adversarial Sequence-to-Sequence Domain Adaptation.

Yaping Zhang, Shuai Nie, Shan Liang

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    This study introduces Adversarial Sequence-to-Sequence Domain Adaptation (ASSDA) to improve text recognition in varied real-world scenarios. ASSDA effectively adapts sequential image data by focusing on character-level alignment, enhancing robustness against domain shifts.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Robust text reading is challenging due to significant variations in real-world image data distributions.
    • Existing domain adaptation methods often fail with sequence-like text images because they treat images holistically, neglecting fine-grained character details.

    Purpose of the Study:

    • To develop a novel domain adaptation method specifically for sequence-like text images.
    • To address the limitations of conventional methods in handling variable-length character information across different domains.

    Main Methods:

    • Propose Adversarial Sequence-to-Sequence Domain Adaptation (ASSDA), a method designed to learn optimal adaptation strategies for sequential image data.
    • Implement an adversarial approach to identify and align critical local regions containing characters across domains.

    Main Results:

    • The proposed ASSDA method demonstrates efficient transfer of sequence knowledge.
    • Experiments validate the effectiveness of ASSDA in addressing diverse domain shifts encountered in real-world text recognition applications.

    Conclusions:

    • ASSDA offers a promising solution for robust text recognition by enabling precise, character-level domain adaptation.
    • The method's ability to focus on "where to adapt" and "how to align" sequential image data enhances its applicability in challenging real-world scenarios.