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Scene Text Detection with Gradient Auto Encoders

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Computer Vision and Image Processing (CVIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1777))

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Abstract

Text serves as an excellent persistent communication medium for unambiguous and precise information exchange. Text could help us to describe any scene. Hence, it would be ideal to understand scene text to accurately identify and understand a scene image or video. Variations such as script, font, color, scale, lighting, angle of view and other distortions make scene text understanding a challenge. Detecting and localizing the possible text, could improve the task of text understanding. Though decades of research had attempted to address the problem, still it is an open area. For instance, requirement of high-performance computation platform, large training dataset and longer training process. We have attempted to train our auto encoder based text detector to precisely localize text with minimum training on a small dataset and limited computational resources. The idea involves computation of morphological gradient to enhance text on the scene image and to feed it to a gradient auto encoder neural network to locate possible text components. The proposed detector can detect text across multiple languages and it is robust against the variations such as scale, orientation, font, and lighting. The results are promising. The proposed method achieves an F-measure of 0.75 and 0.76 on MRRC dataset and MSRA-TD500 dataset respectively, after training with 167 images.

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Authors and Affiliations

  1. Government First Grade College Uppinangady, Uppinangady, 574241, Karnataka, India

    S. Raveeshwara

  2. Department of Computer Science, Mangalore University, Mangalore, 574199, Karnataka, India

    B. H. Shekar

Authors
  1. S. Raveeshwara
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  2. B. H. Shekar
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Corresponding author

Correspondence to S. Raveeshwara .

Editor information

Editors and Affiliations

  1. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Deep Gupta

  2. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Kishor Bhurchandi

  3. Indian Institute of Technology Ropar, Rupnagar, India

    Subrahmanyam Murala

  4. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  5. Indian Institute of Technology Roorkee, Roorkee, India

    Sanjeev Kumar

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Raveeshwara, S., Shekar, B.H. (2023). Scene Text Detection with Gradient Auto Encoders. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_27

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  • DOI: https://doi.org/10.1007/978-3-031-31417-9_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31416-2

  • Online ISBN: 978-3-031-31417-9

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