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UTextNet: A UNet Based Arbitrary Shaped Scene Text Detector

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Intelligent Systems Design and Applications (ISDA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 418))

Abstract

The task of scene text detection has achieved notable success in recent years owing to its wide range of applications, such as automatic entry of information from the image to database, robot sensing, text translation, etc. Many works have already been proposed for horizontal text, and some works for multi-oriented scene text. However, currently, the works of text detection on arbitrarily shaped texts that commonly appear in a natural world environment are scarce. This paper proposed a segmentation-based arbitrary shaped scene text detector adopted from the UNet, called the UTextNet. It comprises of a ResNet-UNet encoder-decoder network where the residual blocks of the ResNet encoder perform features extraction, and UNet decoder module performs the segmentation of the text region. A shallow segmentation head called an approximate binarization (AB) is added for the post-processing task. It performs binarization by using the probability map and threshold map generated by the encoder-decoder framework. The performance of the UTextNet is validated on benchmark datasets, namely ICDAR 2015 and Total-Text, and has demonstrated competitive performance as with the existing state-of-the-art systems.

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Authors and Affiliations

  1. Indian Institute of Information Technology Guwahati, Guwahati, Assam, India

    Veronica Naosekpam, Sushant Aggarwal & Nilkanta Sahu

Authors
  1. Veronica Naosekpam
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  2. Sushant Aggarwal
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  3. Nilkanta Sahu
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Corresponding author

Correspondence to Veronica Naosekpam .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  3. Institut für Wirtschaftsinformatik, Fachhochschule Nordwestschweiz, Olten, Switzerland

    Thomas Hanne

  4. Department of Computer Science and information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Federal University of Bahia, Ondina, Brazil

    Tatiane Nogueira Rios

  6. Nantong University, Nantong Shi, Jiangsu, China

    Weiping Ding

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Naosekpam, V., Aggarwal, S., Sahu, N. (2022). UTextNet: A UNet Based Arbitrary Shaped Scene Text Detector. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_34

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