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Arbitrary Shape Text Detection via Segmentation With Probability Maps.

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    This study introduces a novel segmentation-based method using probability maps for accurate arbitrary shape text detection. The approach effectively handles inaccurate annotations, achieving state-of-the-art performance on multiple benchmarks.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Pattern Recognition

    Background:

    • Arbitrary shape text detection is challenging due to variations in size, aspect ratio, orientation, and shape.
    • Segmentation-based methods show promise for text detection but are hindered by coarse-grained annotations and misclassified pixels.
    • Existing datasets lack precise pixel-level annotations, impacting the performance of current text detection algorithms.

    Purpose of the Study:

    • To develop an innovative and robust segmentation-based method for accurate arbitrary shape text detection.
    • To address the limitations of coarse-grained annotations in scene text detection datasets.
    • To improve the performance of segmentation-based text detection by accurately classifying text pixels.

    Main Methods:

    • Proposed a novel segmentation-based detection method utilizing probability maps.
    • Employed a Sigmoid Alpha Function (SAF) to convert boundary distances to probability maps.
    • Utilized a group of probability maps and an iterative model to learn and assimilate distributions for text reconstruction, followed by region growth algorithms.

    Main Results:

    • Achieved state-of-the-art detection accuracy on several benchmarks.
    • Demonstrated superior F-measure performance on Total-Text (88.79%), CTW1500 (85.75%), and MSRA-TD500 (88.93%) datasets with Watershed post-processing.
    • Showcased promising results on multi-oriented and multilingual scene text detection datasets.

    Conclusions:

    • The proposed probability map-based segmentation method effectively detects arbitrary shape text instances.
    • The approach overcomes challenges posed by inaccurate and coarse-grained annotations.
    • The method demonstrates significant improvements in text detection accuracy and robustness across diverse datasets.