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A New Language-Independent Deep CNN for Scene Text Detection and Style Transfer in Social Media Images.

Palaiahnakote Shivakumara, Ayan Banerjee, Umapada Pal

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 21, 2023
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    Summary

    This study introduces an end-to-end model for detecting text and transferring its style in social media images. The novel approach enhances image quality and character restoration for improved multilingual and cross-language performance.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Natural Language Processing

    Background:

    • Social media images often suffer from quality degradation and diverse languages, making text detection and style transfer challenging.
    • Existing methods struggle with the complexities of real-world social media image data.

    Purpose of the Study:

    • To develop a novel end-to-end model for robust text detection and style transfer in social media images.
    • To address the adverse effects of image quality and arbitrary languages in natural scene text.

    Main Methods:

    • A frequency domain gradient extraction technique is used to identify text candidate points, reducing image quality issues.
    • Text detection is performed using a UNet++ network with an EfficientNet backbone (EffiUNet++).
    • A generative model (TESP-Net) with residual mapping and attention modules handles text style transfer, leveraging recognition results.

    Main Results:

    • The proposed model demonstrates superior performance in text detection and style transfer compared to existing methods.
    • Experiments on social media and benchmark datasets show effectiveness in multilingual and cross-language scenarios.
    • The model successfully restores character structure and details from degraded images.

    Conclusions:

    • The developed end-to-end model offers a significant advancement for text detection and style transfer in challenging social media environments.
    • The frequency domain approach and generative model effectively handle image quality degradation and style variations.
    • The method shows strong potential for applications requiring accurate text recognition and manipulation across diverse languages and image types.