Robust Approach for Product Label Reading

Existing system implements camera-based assistive text reading framework to help blind persons read text labels and product packaging from hand-held objects in their daily lives. Existing work 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. This method extracts moving object region by a mixture-of-Gaussians-based background subtraction method. In the extracted ROI, text localization and recognition are conducted to acquire text information. The existing method propose a novel text localization algorithm by learning gradient features of stroke orientations and distributions of edge pixels in an Adaboost model. In our proposed work our aim is to achieve the better accuracy using proposed algorithm. Present a novel image operator that seeks to find the value of stroke width for each image pixel, and demonstrate its use on the task of text detection in natural images. The proposed operator is local and data dependent, which makes it fast and robust enough to eliminate the need for multi-scale computation or scanning windows.