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Textual Information Access for the Visually Impaired

Textual Information Access for the Visually Impaired. Ramani Duraiswami. 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

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Textual Information Access for the Visually Impaired

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  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.

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