Part of the Lecture Notes in Electrical Engineering book series LNEE, volume Abstract Barcodes are being widely used in many fields of applications of great commercial value, which provide a means of representing data in machine readable format. Various symbologies exist to map the data into barcodes. Image based barcode readers provide many advantages over laser scanners in terms of orientation independence, image archiving and high read rate performance even when barcodes are damaged, distorted, blurred, scratched, low-height and low-contrast. The availability of imaging technology provides a platform for decoding barcode rather than the use of the conventional laser scanner which is lack of mobility. In this paper, image based technique for classification of the given 1-D barcode into respective symbology and its decoding have been proposed.
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See other articles in PMC that cite the published article. Abstract Current camera-based barcode readers do not work well when the image has low resolution, is out of focus, or is motion-blurred. One main reason 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.
Introduction Virtually every packaged good is labeled with at least one form of barcode, generally a flavor of either the EAN or the UPC standards. The success of barcode technology for identification, tracking, and inventory derives from its ability to encode information in a compact fashion with very low associated cost. Commercial laser-based, hand-held barcode scanners achieve robust reading with a reasonable price tag. Recently, however, there has been growing interest in accessing barcodes with regular cellphones rather than with dedicated devices.
Since cellphones are of ubiquitous use, this would enable a multitude of mobile applications. For example, a number of cellphone apps have appeared recently that provide access to the full characteristics and user reviews for a product found at a store, or to on-line price comparisons via barcode reading. Tekin and Coughlan describe a system that detects barcodes in clutter and at a distance, to direct people with visual impairments towards barcodes on packages and decode them [ 11 ].
Unfortunately, the poor quality of the images taken by current cellphone cameras makes it surprisingly difficult to correctly decode barcodes, as shown by the limited performance that many commercial applications exhibit see our tests in Sec. This paper presents a new algorithm for barcode reading that produces excellent results even for images that are blurred, noisy, and with low resolution.
Quantitative comparisons on existing and new barcode image databases show that our technique outperforms other state-of-the-art softwares and compares favorably with other reported results.
A unique characteristic of our algorithm is that it never performs binarization of the graylevel brightness profile before processing. We argue that this early-commitment operation, executed by virtually all existing algorithms, translates into unrecoverable information loss, thus complicating all further processing.
This is especially the case for low-resolution images, where binarization errors may have catastrophic effects. For example, Fig.
Reading Challenging Barcodes with Cameras
Abstract: Automatic identification technology such as RFID promises to connect physical objects with virtual representations or even computational capabilities. However, even though RFID tags are continuously falling in price, their widespread use on consumer items is still several years away, rende Much more ubiquitous are printed bar codes, yet so far their recognition required either specialized scanner equipment, custom-tailored bar codes or costly commercial licenses — all equally significant deployment hurdles. These approaches are often used in professional image recognition software, as the offer very good recognition rates.
A low cost optical barcode reader using a webcam
See other articles in PMC that cite the published article. Abstract Current camera-based barcode readers do not work well when the image has low resolution, is out of focus, or is motion-blurred. One main reason 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.
Classification and Decoding of Barcodes: An Image Processing Approach
See other articles in PMC that cite the published article. It would be very convenient for consumers to be able to read these barcodes using portable cameras e. 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. 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. Introduction The 1D barcode was developed as a package label that could be swiftly and accurately read by a laser scanner.