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Scene Text Localization Using Lightweight Convolutional Networks

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Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1474))

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Abstract

Various research initiatives have been reported regarding highly effective results for the text detection problem, which consists of detecting textual elements, such as words and phrases, in digital images. Text localization is an important step on very widely used mobile applications, for instance, on-the-go translations and recognition of text for the visually impaired. At the same time, edge computing is revolutionizing the way embedded systems are architected by moving complex processing and analysis to end devices (e.g., mobile and wearable devices). In this context, the development of lightweight networks that can be run in devices with restricted computing power and with a minimum latency as possible is essential to make plenty of mobile-oriented solutions feasible in practice. In this work, we investigate the use of efficient object detection networks to address this task, proposing the fusion of two lightweight neural network architectures, MobileNetV2 and Single Shot Detector (SSD), into our approach named MobText. As experimental results in the ICDAR’11 and ICDAR’13 datasets demonstrates that our solution yields the best trade-off between effectiveness and efficiency in terms of processing time, achieving the state-of-the-art results on the ICDAR’11 dataset with an F-measure of \(96.09\%\) and an average processing time of 464 ms on a smartphone device, over experiments executed on both dataset images and with images captured in real time from the portable device.

Part of the results presented in this work were obtained through the “Algoritmos para Detecção e Reconhecimento de Texto Multilíngue” project, funded by Samsung Eletrônica da Amazônia Ltda., under the Brazilian Informatics Law 8.248/91.

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Author information

Authors and Affiliations

  1. Institute of Computing, University of Campinas, Campinas, 13083-852, Brazil

    Luis Gustavo Lorgus Decker, Allan Pinto, Jose Luis Flores Campana, Manuel Cordova Neira, Andreza Aparecida dos Santos, Jhonatas Santos de Jesus Conceição & Helio Pedrini

  2. AI R&D Lab, Samsung R&D Institute Brazil, Campinas, 13097-160, Brazil

    Marcus de Assis Angeloni, Lin Tzy Li & Diogo Carbonera Luvizon

  3. Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), Ålesund, Norway

    Ricardo da S. Torres

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  1. Luis Gustavo Lorgus Decker
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Editor information

Editors and Affiliations

  1. IRISA, University of Rennes 1, Rennes, France

    Kadi Bouatouch

  2. Universidade do Porto, Porto, Portugal

    A. Augusto de Sousa

  3. University of Genova, Genova, Italy

    Manuela Chessa

  4. Mines ParisTech, Paris, France

    Alexis Paljic

  5. Linnaeus University, Växjö, Sweden

    Andreas Kerren

  6. French Civil Aviation University (ENAC), Toulouse, France

    Christophe Hurter

  7. Università di Catania, Catania, Italy

    Giovanni Maria Farinella

  8. Universitat de Barcelona, Barcelona, Spain

    Petia Radeva

  9. Escola Superior de Tecnologia de Setúbal, Setúbal, Portugal

    Jose Braz

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Decker, L.G.L. et al. (2022). Scene Text Localization Using Lightweight Convolutional Networks. In: Bouatouch, K., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2020. Communications in Computer and Information Science, vol 1474. Springer, Cham. https://doi.org/10.1007/978-3-030-94893-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-94893-1_13

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