Please use this identifier to cite or link to this item:
|Publication type:||Conference paper|
|Type of review:||Peer review (publication)|
|Title:||Accessible PDFs : applying artificial intelligence for automated remediation of STEM PDFs|
|Authors:||Schmitt-Koopmann, Felix M.|
Huang, Elaine M.
|Proceedings:||ASSETS '22 Proceedings|
|Editors of the parent work:||Froehlich, Jon|
|Conference details:||24th International ACM SIGACCESS Conference on Computers and Accessibility, Athens, Greece, 23-26 October 2022|
|Publisher / Ed. Institution:||Association for Computing Machinery|
|Subjects:||Accessibility; PDF accessibility; Tagged PDF; PDF/UA; Math viewer; Document analysis; Formula recognition; Page object detection; Reading order|
|Subject (DDC):||006: Special computer methods|
|Abstract:||People with visual impairments use assistive technology, e.g., screen readers, to navigate and read PDFs. However, such screen readers need extra information about the logical structure of the PDF, such as the reading order, header levels, and mathematical formulas, described in readable form to navigate the document in a meaningful way. This logical structure can be added to a PDF with tags. Creating tags for a PDF is time-consuming, and requires awareness and expert knowledge. Hence, most PDFs are left untagged, and as a result, they are poorly readable or unreadable for people who rely on screen readers. STEM documents are particularly problematic with their complex document structure and complicated mathematical formulae. These inaccessible PDFs present a major barrier for people with visual impairments wishing to pursue studies or careers in STEM fields, who cannot easily read studies and publications from their field. The goal of this Ph.D. is to apply artificial intelligence for document analysis to reasonably automate the remediation process of PDFs and present a solution for large mathematical formulae accessibility in PDFs. With these new methods, the Ph.D. research aims to lower barriers to creating accessible scientific PDFs, by reducing the time, effort, and expertise necessary to do so, ultimately facilitating greater access to scientific documents for people with visual impairments.|
|Fulltext version:||Published version|
|License (according to publishing contract):||CC BY 4.0: Attribution 4.0 International|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Applied Information Technology (InIT)|
|Appears in collections:||Publikationen School of Engineering|
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|2022_SchmittKoopmann-etal_Accessible-PDFs-STEM-fields.pdf||304.46 kB||Adobe PDF|
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Schmitt-Koopmann, F. M., Huang, E. M., & Darvishy, A. (2022). Accessible PDFs : applying artificial intelligence for automated remediation of STEM PDFs [Conference paper]. In J. Froehlich, K. Shinohara, & S. Ludi (Eds.), ASSETS ’22 Proceedings (p. 90). Association for Computing Machinery. https://doi.org/10.1145/3517428.3550407
Schmitt-Koopmann, F.M., Huang, E.M. and Darvishy, A. (2022) ‘Accessible PDFs : applying artificial intelligence for automated remediation of STEM PDFs’, in J. Froehlich, K. Shinohara, and S. Ludi (eds) ASSETS ’22 Proceedings. Association for Computing Machinery, p. 90. Available at: https://doi.org/10.1145/3517428.3550407.
F. M. Schmitt-Koopmann, E. M. Huang, and A. Darvishy, “Accessible PDFs : applying artificial intelligence for automated remediation of STEM PDFs,” in ASSETS ’22 Proceedings, 2022, p. 90. doi: 10.1145/3517428.3550407.
Schmitt-Koopmann, Felix M., et al. “Accessible PDFs : Applying Artificial Intelligence for Automated Remediation of STEM PDFs.” ASSETS ’22 Proceedings, edited by Jon Froehlich et al., Association for Computing Machinery, 2022, p. 90, https://doi.org/10.1145/3517428.3550407.
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