Axessibility: a LaTeX Package for Mathematical Formulae Accessibility in PDF Documents

Accessing mathematical formulae within digital documents is challenging for blind people. In particular, document formats designed for printing, such as PDF, structure math content for visual access only. While accessibility features exist to present PDF content non-visually, formulae support is limited to providing replacement text that can be read by a screen reader or displayed on a braille bar. However, the operation of inserting replacement text is left to document authors, who rarely provide such content. Furthermore, at best, description of the formulae are provided. Thus, conveying detailed understanding of complex formulae is nearly impossible. In this contribution we report our ongoing research on Axessibility, a LATEX package framework that automates the process of making mathematical formulae accessible by providing the formulae LATEX code as PDF replacement text. Axessibility is coupled with external scripts to automate its integration in existing documents, expand user shorthand macros to standard LATEX representation, and custom screen reader dictionaries that improve formulae reading on screen readers

AudioFunctions.web: Multimodal Exploration of Mathematical Function Graphs

We present AudioFunctions.web, a web app that uses sonifcation, earcons and speech synthesis to enable blind people to explore mathematical function graphs. The system is designed for personalized access through different interfaces (touchscreen, keyboard, touchpad and mouse) on both mobile and traditional devices, in order to better adapt to different user abilities and preferences. It is also publicly available as a web service and can be directly accessed from the teaching material through a hypertext link. An experimental evaluation with 13 visually impaired participants highlights that, while the usability of all the presented interaction modalities is high, users with different abilities prefer different interfaces to interact with the system. It is also shown that users with higher level of mathematical education are capable of better adapting to interaction modalities considered more diffcult by others

Aprendizado de máquina aplicado para melhorar a acessibilidade de documentos PDF para usuários com deficiência visual

Orientador: Luiz Cesar MartiniDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Os documentos digitais são acessados por pessoas com deficiência visual (VIP) por meio de leitores de tela. Tradicionalmente, os documentos digitais eram traduzidos para texto em braille, mas os leitores de tela provaram ser eficientes para a aquisição de conhecimento para as VIP. No entanto, os leitores de tela e outras tecnologias assistivas têm limitações significativas quando existem tabelas em documentos digitais como os documentos PDF (Portable Document Format). Por exemplo, os leitores de tela não podem seguir a sequência de leitura correta da tabela com base em sua estrutura visual causando que esse conteúdo seja inacessível aos VIP. Para lidar com esse problema, neste trabalho, desenvolvemos um sistema para a recuperação de informações de tabela de documentos PDF para uso em leitores de tela usados por pessoas com deficiência visual. A metodologia proposta aproveita as técnicas de visão computacional com uma abordagem de aprendizado profundo para tornar os documentos acessíveis em vez da abordagem clássica de programação baseada em regras. Explicamos em detalhe a metodologia que usamos e como avaliar objetivamente a abordagem por meio de métricas de entropia, ganho de informação e pureza. Os resultados mostram que nossa metodologia proposta pode ser usada para reduzir a incerteza experimentada por pessoas com deficiência visual ao ouvir o conteúdo das tabelas em documentos digitais através de leitores de tela. Nosso sistema de recuperação de informações de tabela apresenta duas melhorias em comparação com as abordagens tradicionais de marcação de arquivos PDF. Primeiro, nossa abordagem não requer supervisão de pessoas com visão. Segundo, nosso sistema é capaz de trabalhar com PDFs baseados em imagem e em textoAbstract: Digital documents are accessed by visually impaired people (VIP) through screen readers. Traditionally, digital documents were translated to braille text, but screen readers have proved to be efficient for the acquisition of digital document knowledge by VIP. However, screen readers and other assistive technologies have significant limitations when there exist tables in digital documents such as portable document format (PDF). For instance, screen readers can not follow the correct reading sequence of the table based on its visual structure causing this content is inaccessible for VIP. In order to deal with this problem, in this work, we developed a system for the retrieval of table information from PDF documents for use in screen readers used by visually impaired people. The proposed methodology takes advantage of computer vision techniques with a deep learning approach to make documents accessible instead of the classical rule-based programming approach. We explained in detail the methodology that we used and how to objectively evaluate the approach through entropy, information gain, and purity metrics. The results show that our proposed methodology can be used to reduce the uncertainty experienced by visually impaired people when listening to the contents of tables in digital documents through screen readers. Our table information retrieval system presents two improvements compared with traditional approaches of tagging text-based PDF files. First, our approach does not require supervision by sighted people. Second, our system is capable of working with image-based as well as text-based PDFsMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric