1. Textual Information Access for the Visually Impaired Ramani Duraiswami

2. Project Approximately 1 million blind and 5 million visually impaired people in the US –Comparable fractions of the population elsewhere Their Goal: Lead an independent productive life –To do this they need to understand a significant amount of textual information from the environment Examples –Signs on streets, supermarkets/groceries, buildings –Labels on medicines, products –Instructions on equipment, computers –Newspapers, magazines Our goal: Provide them with devices to read text –Computer vision, Pattern recognition (OCR), computational audio, and wearable computers to help them.

3. Restricted initial goal: magazine reader Tool targeted to people with some vision Use optical character recognition (OCR) software –OCR works with flat, high resolution scanned text. Read out the recognized text with text-to-speech (TTS) software Goal: read text with a video camera, OCR software, portable/wearable computer, headphones Problems – Text is on a surface that is mostly planar or curved – Warped by perspective projection to the camera – Camera images are low resolution (640 x 480) – OCR uses scanned images at 600 to 2400 dpi

4. System Design For good OCR –Characters must be 30-60 pixels tall –Characters must not be skewed Camera captures initial image of page Using initial images –Estimate font size and page structure –Determine scanning pattern Zoom into pieces of the image –Register each zoomed piece with the big picture –Capture image at optimum focus and zoom To make sure pieces are not too small –Use super-resolution techniques Mosaic pieces together and dewarp

5. Super-resolution Convert a sequence of many (different) coarsely sampled images into a high resolution image.