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Understand Layout and Translate Text: Unified Feature-Conductive End-to-End Document Image Translation.

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    This study introduces Unified Document Image Translation (UniDIT), a new framework that explicitly integrates document layout and text for more accurate translation. UniDIT enhances document image translation by prioritizing layout understanding before text translation.

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    Area of Science:

    • Computer Vision
    • Natural Language Processing
    • Machine Translation

    Background:

    • Document Image Translation (DIT) is a complex multimodal task requiring integration of text and layout.
    • Existing DIT methods struggle with either error accumulation from separate processing or insufficient layout incorporation in end-to-end models.

    Purpose of the Study:

    • To develop a novel framework that explicitly and effectively incorporates document layout into the translation process.
    • To improve the accuracy and robustness of Document Image Translation.

    Main Methods:

    • Introduced a pipeline prioritizing layout understanding (geometric and logical) before text translation.
    • Developed Unified Document Image Translation (UniDIT), a unified network integrating layout-specific modules.
    • Implemented a novel bridging mechanism to adapt layout features for translation.

    Main Results:

    • UniDIT demonstrates superior performance across various benchmarks, including the new DITransv2 dataset.
    • The framework effectively leverages geometric and logical layout information for improved translation.
    • Outperforms previous state-of-the-art methods in all evaluated aspects of Document Image Translation.

    Conclusions:

    • The proposed UniDIT framework offers a significant advancement in Document Image Translation by unifying layout and text processing.
    • Explicitly addressing and prioritizing layout information is crucial for effective DIT.
    • The DITransv2 benchmark facilitates further research in complex document image translation.