ABSTRACT are: 1) a novel motion-based algorithm to


 We propose a
camera-based assistive text reading framework to help blind persons read text
labels and product packaging from hand-held objects in their daily lives. We
first propose an efficient and effective motion based method to define a region
of interest (ROI) in the video by asking the user to shake the object. To
automatically localize the text regions from the object ROI, we propose a novel
text localization algorithm by learning gradient features of stroke
orientations and distributions of edge pixels in an Ada boost model. Text
characters in the localized text regions are then binarized and recognized by
off-the-shelf optical character recognition (OCR) software. The recognized text codes are output to
blind users in speech.

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Keywords-Assistive text, blind
persons, distribution of edge pixels, hand-held objects, optical
character recognition (OCR), stroke orientation, text localization.



       Of the 314 million
visually impaired people
worldwide, 45 million are blind. Reading is obviously
essential in today’s society. Printed
text is everywhere
in the form of reports, receipts, bank statements, restaurant menus, classroom handouts, product packages, instructions on medicine bottles,
etc. Today, there are already a few systems that have some
promise for portable use, but they cannot handle product labeling. For example,
portable bar code readers designed to help blind people identify different
products in an extensive product database can enable users who are blind to
access information about these products through speech and Braille. But a big
limitation is that it is very hard for blind users to find the position of the
bar code and to correctly point the bar code reader at the bar code.

Our main contributions
embodied in this prototype system are: 1) a novel motion-based algorithm
to solve the aiming
problem for blind users by their simply
shaking the object
of interest for a brief period; 2) a novel algorithm
of automatic text localization to extract
text regions from complex background and multiple text patterns


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