  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 life. To isolate the object from untidy backgrounds or other surrounding
objects in the camera vision, we initially propose an efficient and effective
motion based method to define a region of interest (ROI) in the video by asking
the user to tremble the object. This scheme extracts moving object region by a
mixture-of-Gaussians-based background subtraction technique. In the extracted
ROI, text localization and recognition are conducted to acquire text details.
To automatically focus the text regions from the object ROI, we offer a novel
text localization algorithm by learning gradient features of stroke
orientations and distributions of edge pixels in an Adaboost model. Text
characters in the localized text regions are then binarized and recognized by
off-the-shelf optical character identification software. The renowned text
codes are converted into audio output to the blind users. Performance of the
suggested text localization algorithm is quantitatively evaluated on ICDAR-2003
and ICDAR-2011 Robust Reading Datasets. Experimental results demonstrate that
our algorithm achieves the highest level of developments at present time. The
proof-of-concept example is also evaluated on a dataset collected using ten
blind persons to evaluate the effectiveness of the scheme. We explore the user
interface issues and robustness of the algorithm in extracting and reading text
from different objects with complex backgrounds.
