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One unsolved sub-task of document analysis is mathematical formula detection (MFD).Research by ourselves and others has shown that existing MFD datasets with inline and display formula labels are small and have insufficient labeling quality. There is therefore an urgent need for datasets with better quality labeling for future research in the MFD field, as they have a high impact on the performance of the models trained on them. We present an advanced labeling pipeline and a new dataset called FormulaNet in this paper. At over 45k pages, we believe that FormulaNet is the largest MFD dataset with inline formula labels. Our experiments demonstrate substantially improved labeling quality for inline and display formulae detection over existing datasets. Additionally, we provide a math formula detection baseline for FormulaNet with an mAP of 0.754. Our dataset is intended to help address the MFD task and may enable the development of new applications, such as making mathematical formulae accessible in PDFs for visually impaired screen reader users.

This paper compares accessibility features of two popular platforms from a user perspective. The comparison is based on accessibility features for different kinds of disabilities such as vision, hearing or physically challenged users. A section on accessibility in mobile applications follows. According to a survey [1], the use of mobile platforms by people with disabilities is dramatically increasing. New accessibility features are introduced for each release of these platforms which makes them an affordable assistive technology.