Software System for Vocal Rendering of Printed Documents

The objective of this paper is to present a software system architecture developed to render the printed documents in a vocal form. On the other hand, in the paper are described the software solutions that exist as software components and are necessary for documents processing as well as for multimedia device controlling used by the system. The usefulness of this system is for people with visual disabilities that can access the contents of documents without that they be printed in Braille system or to exist in an audio form.accessibility, TWAIN, OCR, TTS, SAPI

Pickup usability dominates: a brief history of mobile text entry research and adoption

Text entry on mobile devices (e.g. phones and PDAs) has been a research challenge since devices shrank below laptop size: mobile devices are simply too small to have a traditional full-size keyboard. There has been a profusion of research into text entry techniques for smaller keyboards and touch screens: some of which have become mainstream, while others have not lived up to early expectations. As the mobile phone industry moves to mainstream touch screen interaction we will review the range of input techniques for mobiles, together with evaluations that have taken place to assess their validity: from theoretical modelling through to formal usability experiments. We also report initial results on iPhone text entry speed

Image processing for the extraction of nutritional information from food labels

Current techniques for tracking nutritional data require undesirable amounts of either time or man-power. People must choose between tediously recording and updating dietary information or depending on unreliable crowd-sourced or costly maintained databases. Our project looks to overcome these pitfalls by providing a programming interface for image analysis that will read and report the information present on a nutrition label directly. Our solution involves a C++ library that combines image pre-processing, optical character recognition, and post-processing techniques to pull the relevant information from an image of a nutrition label. We apply an understanding of a nutrition label\u27s content and data organization to approach the accuracy of traditional data-entry methods. Our system currently provides around 80% accuracy for most label images, and we will continue to work to improve our accuracy