Accessible images (AIMS): a model to build self-describing images for assisting screen reader users

Abstract

Non-visual web access depends on the textual description of various non-text elements of web pages. The existing methods of describing images for non-visual access do not provide a strong coupling between described images and their description. If an image is reused multiple times either in a single Web site or across multiple times, it is required to keep the description at all instances. This paper presents a tightly coupled model termed accessible images (AIMS) which utilizes a steganography-based approach to embed the description in the images at the server side and updating alt text of the web pages with the description extracted with the help of a browser extension. The proposed AIMS model has been built, targeting toward a web image description ecosystem in which images evolve into a self-description phase. The primary advantage of the proposed AIMS model is the elimination of the redundant description of an image resource at multiple instances. The experiments conducted on a dataset confirm that the AIMS model is capable of embedding and extracting descriptions with an accuracy level of 99.6%.

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Notes

  1. 1.

    https://pypi.python.org/pypi/PIL.

  2. 2.

    https://github.com/shakoorst/AIMS-Dataset.

  3. 3.

    The machine used for the test was an Intel Pentium (Core 2 Duo) with 2 GB of RAM and an Internet connection of 512 Kbps.

  4. 4.

    http://github.com/jamesturk/jellyfish.

  5. 5.

    https://github.com/jterrace/pyssim.

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Correspondence to K. S. Kuppusamy.

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Nengroo, A.S., Kuppusamy, K.S. Accessible images (AIMS): a model to build self-describing images for assisting screen reader users. Univ Access Inf Soc 17, 607–619 (2018). https://doi.org/10.1007/s10209-017-0607-z

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Keywords

  • Image accessibility
  • Alternative text
  • Steganography
  • Least significant bit (LSB)
  • Browser extension