Orazio Gallo, Roberto Manduchi
08/18/2009 09:00 AM
Computer Engineering
Reading barcodes with standard cameras, such as cell phone cameras, enables interesting opportunities for ubiquitous computing. Unfortunately, current camera-based barcode readers do not work well when the image has low resolution, is out of focus, or is blurred due to motion. One main reason for this poor performance is that virtually all existing algorithms perform some sort of binarization, either by gray scale thresholding or by finding the bar edges. We propose a new approach to barcode reading that never needs to binarize the image. Instead, we use deformable barcode digit models in a maximum likelihood setting. We show that the particular nature of these models enables efficient integration over the space of deformations. Global optimization over all digits is then performed using dynamic programming. Experiments with challenging UPC-A barcode images show substantial improvement over other state-of-the-art algorithms.
