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ASTER: An Attentional Scene Text Recognizer with Flexible Rectification.

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    ASTER, a novel end-to-end neural network, effectively rectifies and recognizes distorted scene text. This system achieves state-of-the-art performance, enhancing scene text recognition capabilities.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Scene text recognition is challenged by distorted or irregularly laid out text, particularly perspective and curved text common in natural scenes.
    • Existing methods struggle with these text variations, limiting recognition accuracy.

    Purpose of the Study:

    • To introduce ASTER, an end-to-end neural network model designed to address the challenges of recognizing distorted scene text.
    • To develop a system capable of adaptively rectifying irregular text before recognition.

    Main Methods:

    • ASTER employs a two-part architecture: a rectification network and a recognition network.
    • The rectification network utilizes a flexible Thin-Plate Spline transformation, trained without human annotations, to correct text distortions.
    • The recognition network is an attentional sequence-to-sequence model that predicts text directly from the rectified image.

    Main Results:

    • Extensive experiments demonstrate the effectiveness of ASTER's rectification component.
    • ASTER achieves state-of-the-art performance in scene text recognition tasks.
    • The model proves to be a valuable component for enhancing end-to-end recognition systems.

    Conclusions:

    • ASTER successfully rectifies and recognizes distorted scene text, overcoming common challenges.
    • The proposed end-to-end system achieves superior recognition accuracy.
    • ASTER's rectification capability significantly enhances overall scene text recognition systems.