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DCT-DWT-FFT Based Method for Text Detection in Underwater Images

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Pattern Recognition (ACPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13189))

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Abstract

Text detection in underwater images is an open challenge because of the distortions caused by refraction, absorption of light, particles, and variations depending on depth, color, and nature of water. Unlike existing methods aimed at text detection in natural scene images, in this paper, we have proposed a novel method for text detection in underwater images through a new enhancement model. Based on observations that fine details of text in image share with high energy, spatial resolution, and brightness, we consider Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Fast Fourier Transform (FFT) for image enhancement to highlight the text features. The enhanced image is fed to a modified Character Region Awareness for Text Detection (CRAFT) model to detect text in underwater images. To explore enhancement methods, we evaluate six combinations of image enhancement techniques, namely, DCT-DWT-FFT, DCT-FFT-DWT, DWT-DCT-FFT, DWT-FFT-DCT, FFT-DCT-DWT, FFT-DWT-DCT. Experimental results on our dataset of underwater images and benchmark datasets of natural scene text detection, namely, MSRA-TD500, ICDAR 2019 MLT, ICDAR 2019 ArT, Total-Text, CTW1500, and COCO Text show that the proposed method performs well for both underwater and natural scene images and outperforms the existing methods on all the datasets.

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Acknowledgements

The work of Cheng-Lin Liu was supported by the National Key Research and Development Program under Grant No. 2018AAA0100400 and the National Natural Science Foundation of China under Grant No. 61721004. This work also partially supported by TIH, ISI, Kolkata.

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Authors and Affiliations

  1. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    Ayan Banerjee, Soumyajit Pal & Umapada Pal

  2. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Palaiahnakote Shivakumara

  3. National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  4. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

Authors
  1. Ayan Banerjee
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  2. Palaiahnakote Shivakumara
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  3. Soumyajit Pal
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  4. Umapada Pal
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  5. Cheng-Lin Liu
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Corresponding author

Correspondence to Palaiahnakote Shivakumara .

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Editors and Affiliations

  1. Korea University, Seoul, Korea (Republic of)

    Christian Wallraven

  2. Nanjing University, Nanjing, China

    Qingshan Liu

  3. Osaka University, Osaka, Japan

    Hajime Nagahara

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Banerjee, A., Shivakumara, P., Pal, S., Pal, U., Liu, CL. (2022). DCT-DWT-FFT Based Method for Text Detection in Underwater Images. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13189. Springer, Cham. https://doi.org/10.1007/978-3-031-02444-3_16

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  • DOI: https://doi.org/10.1007/978-3-031-02444-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02443-6

  • Online ISBN: 978-3-031-02444-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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