1d Bar Code Reading on Camera Phones

Abstract

The availability of camera phones provides people with a mobile platform for decoding bar codes, whereas conventional scanners lack mobility. However, using a normal camera phone in such applications is challenging due to the out-of-focus problem. In this paper, we present the research effort on the bar code reading algorithms using a VGA camera phone, NOKIA 7650. EAN-13, a widely used 1D bar code standard, is taken as an example to show the efficiency of the method. A wavelet-based bar code region location and knowledge-based bar code segmentation scheme is applied to extract bar code characters from poor-quality images. All the segmented bar code characters are input to the recognition engine, and based on the recognition distance, the bar code character string with the smallest total distance is output as the final recognition result of the bar code. In order to train an efficient recognition engine, the modified Generalized Learning Vector Quantization (GLVQ) method is designed for optimizing a feature extraction matrix and the class reference vectors. 19 584 samples segmented from more than 1000 bar code images captured by NOKIA 7650 are involved in the training process. Testing on 292 bar code images taken by the same phone, the correct recognition rate of the entire bar code set reaches 85.62%. We are confident that auto focus or macro modes on camera phones will bring the presented method into real world mobile use.

Neural 1D Barcode Detection Using the Hough Transform

“…• ZXing-6rot denotes an algorithm obtained by applying 6 times the original ZXing algorithm to 6 different angles for every processed image. The average time required by ZXing-6rot to process an image is equal to 6 times the time required to process a single angle using ZXing: 732 ms. OurAN obtains better results than OurBB since, as expected from the results presented in Table 1, some bounding boxes are not correctly detected by our bounding box detection method (Section 3.2), therefore the scan lines traced over the rows intersecting those bounding boxes in the original do not produce any results.…”

“…The task of reading 1D barcodes from camera captured images has been approached in different ways [6], [7], [8], [9], [10] ignazio.gallo@uninsubria.it [11], [12]. Most of the currently available barcode reading mobile applications analyze the gray intensity profiles of single lines in the given images to identify typical patterns associated with the parallel bars of 1D barcodes, thus they usually require the user to place the barcodes in a specific position within the camera screen [12].…”

Blur-resistant joint 1D and 2D barcode localization for smartphones

“…Examples include [10,7]. We consider these methods to be too complex for real-time barcode scanning on smartphones.…”

Reading 1D Barcodes with Mobile Phones Using Deformable Templates

“…Keeping localization and decoding distinct allows for a higher computational efficiency. Existing approaches for barcode localization apply to the binarized image methods based on Hough transforms [6], edge orientation histograms [14], morphological operators [4], or wavelet transforms [13]. Other approaches assume that the center of the image falls within the barcode area [7], [12], thus greatly simplifying the problem: Ohbuchi et al reduce the problem to one of finding the angular direction of the bars [7], while Wachenfeld et al extract a scanline thought the center of the image and binarize it with an adaptive threshold; the endpoints are localized by counting the number of bars detected [12].…”