Removing motion blur from barcode images

Camera shake during photography is a common problem which causes images to get blurred. Here we choose a specific problem in which the image is a barcode and the motion can be modeled as a convolution. We design a blind deconvolution algorithm to remove the translatory motion from a blurred barcode image. Based on the bimodal characteristics of barcode image histograms, we construct a simple target function that measures how similar a deconvoluted image is to a barcode. We minimize this target function over the set of possible convolution kernels to find the most likely blurring kernel. By restricting our search to dome-shaped kernels (first monotonously increasing and then monotonously decreasing) we decrease the number of false solutions. We have tried our system on a collection of a 138 barcode images with varying camera blur, and the recognition rate increases from 32% to 65%.