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Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation.

Maoyuan Ye, Jing Zhang, Juhua Liu

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    Summary
    This summary is machine-generated.

    Hi-SAM leverages the Segment Anything Model (SAM) for advanced hierarchical text segmentation and layout analysis across four levels: pixel, word, text-line, and paragraph. This unified model achieves state-of-the-art results with efficient training and flexible inference modes.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • The Segment Anything Model (SAM) has advanced general segmentation capabilities.
    • Hierarchical text segmentation and layout analysis remain challenging tasks.
    • Existing models often require specialized architectures for different text levels.

    Purpose of the Study:

    • To introduce Hi-SAM, a unified model for hierarchical text segmentation and layout analysis.
    • To adapt SAM for high-quality pixel-level text segmentation.
    • To enable efficient and accurate segmentation across multiple text hierarchies.

    Main Methods:

    • Parameter-efficient fine-tuning of SAM for pixel-level text segmentation (TS).
    • Iterative semi-automatic label generation for unifying hierarchical text data.
    • Development of an end-to-end trainable Hi-SAM with a hierarchical mask decoder.
    • Implementation of Automatic Mask Generation (AMG) and Promptable Segmentation (PS) modes.

    Main Results:

    • Achieved state-of-the-art pixel-level text segmentation with high fgIOU scores on Total-Text and TextSeg datasets.
    • Demonstrated significant improvements in hierarchical text segmentation and layout analysis (PQ and F1 scores) on the HierText dataset.
    • Hi-SAM requires fewer training epochs compared to previous specialized models.

    Conclusions:

    • Hi-SAM provides a unified and effective solution for hierarchical text segmentation and layout analysis.
    • The model demonstrates strong performance across multiple text granularities.
    • Hi-SAM offers flexible inference modes, enhancing its practical applicability.