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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Fuzzy Semantics for Arbitrary-Shaped Scene Text Detection.

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    This study introduces a novel redundancy removal strategy for accurately detecting arbitrary-shaped scene texts. By focusing on fuzzy text and separatrix semantics, it effectively separates cluttered instances without iterative seed expansion.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Pattern Recognition

    Background:

    • Bottom-up methods are common for detecting arbitrary-shaped scene texts due to their flexibility.
    • Segmentation-based methods struggle to separate adjacent text instances with homogeneous textures and cluttered distributions.
    • Existing seed expansion strategies for text separation can lead to error accumulation through iterative processing.

    Purpose of the Study:

    • To propose a more straightforward and robust method for detecting arbitrary-shaped scene texts.
    • To avoid the limitations of seed area segmentation and iterative processing in text instance separation.
    • To effectively separate cluttered scene text instances by directly exploring fuzzy semantics.

    Main Methods:

    • A redundancy removal strategy is proposed, directly exploring fuzzy semantics of 'text' and 'separatrix'.
    • Cluttered instances are separated by excluding 'separatrix' pixels from text regions.
    • Reliability analysis is conducted during optimization and inference to mitigate false positives at ambiguous boundaries.

    Main Results:

    • The proposed method effectively separates adjacent and cluttered scene text instances.
    • Experiments on benchmark datasets demonstrate the effectiveness of the redundancy removal strategy.
    • The approach successfully handles fuzzy semantic boundaries in scene text detection.

    Conclusions:

    • The redundancy removal strategy offers a more direct and less error-prone alternative to seed expansion for scene text detection.
    • Directly modeling fuzzy semantics and employing reliability analysis enhances the robustness of text instance separation.
    • This method shows significant promise for improving arbitrary-shaped scene text detection in complex environments.