The 1D barcode is a ubiquitous labeling technology, with symbologies such as UPC used to label approximately 99% of all packaged goods in the US. It would be very convenient for consumers to be able to read these barcodes using portable cameras (e.g. mobile phones), but the limited quality and resolution of images taken by these cameras often make it difficult to read the barcodes accurately. We propose a Bayesian framework for reading 1D barcodes that models the shape and appearance of barcodes, allowing for geometric distortions and image noise, and exploiting the redundant information contained in the parity digit. An important feature of our framework is that it doesn’t require that every barcode edge be detected in the image. Experiments on a publicly available dataset of barcode images explore the range of images that are readable, and comparisons with two commercial readers demonstrate the superior performance of our algorithm.
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