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Scene Text Recognition: No Country for Old Men?

  • Conference paper
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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9009))

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Abstract

It is a generally accepted fact that Off-the-shelf OCR engines do not perform well in unconstrained scenarios like natural scene imagery, where text appears among the clutter of the scene. However, recent research demonstrates that a conventional shape-based OCR engine would be able to produce competitive results in the end-to-end scene text recognition task when provided with a conveniently preprocessed image. In this paper we confirm this finding with a set of experiments where two off-the-shelf OCR engines are combined with an open implementation of a state-of-the-art scene text detection framework. The obtained results demonstrate that in such pipeline, conventional OCR solutions still perform competitively compared to other solutions specifically designed for scene text recognition.

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Notes

  1. 1.

    Word Lens and the Google Translate service are examples of a real applications of end-to-end scene text detection and recognition that have acquired market-level maturity.

  2. 2.

    http://code.google.com/p/tesseract-ocr/.

  3. 3.

    OCR Omnipage Professional, available at http://www.nuance.com/.

  4. 4.

    Word list is provided by the Microsoft Web N-Gram Service (http://webngram.research.microsoft.com/info/) with top 100k frequently searched words on the Bing search engine.

  5. 5.

    http://docs.opencv.org/trunk/modules/text/doc/erfilter.html.

  6. 6.

    http://code.google.com/p/tesseract-ocr/.

  7. 7.

    http://finereader.abbyy.com/.

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Acknowledgement

This project was supported by the Spanish project TIN2011-24631 the fellowship RYC-2009-05031, and the Catalan government scholarship 2013 FI1126. The authors want to thanks also Google Inc. for the support received through the GSoC project, as well as the OpenCV community, specially to Stefano Fabri and Vadim Pisarevsky, for their help in the implementation of the scene text detection module evaluated in this paper.

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Authors and Affiliations

  1. Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, Spain

    Lluís Gómez & Dimosthenis Karatzas

Authors
  1. Lluís Gómez
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  2. Dimosthenis Karatzas
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Correspondence to Lluís Gómez .

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Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Gómez, L., Karatzas, D. (2015). Scene Text Recognition: No Country for Old Men?. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_12

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  • DOI: https://doi.org/10.1007/978-3-319-16631-5_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16630-8

  • Online ISBN: 978-3-319-16631-5

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