A ROBUST SOFTWARE BARCODE READER USING THE HOUGH TRANSFORM PDF

A Robust Software Barcode Reader Using the Hough Transform . In this paper we present a method based on the Hough transform which. Published in: · Proceeding. ICIIS ’99 Proceedings of the International Conference on Information Intelligence and Systems. Page March 31 – April A Robust Software Barcode Reader Using the Hough Transform (Englisch). Muniz, R. / Junco, L. / Otero, A. / Institute of Electrical and Electronics Engineers.

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Therefore, in order to localize the endpoints o L and o R of the barcode, we first determine the intersections i L and i R of the scanline l n with the rectangle and tfansform, the rectangle being larger than the actual barcode, we proceed inwards from each end see Fig. We are currently investigating the use of our algorithm integrated in a hardware based platform to achieve higher performance.

Reading 1-D Barcodes with Mobile Phones Using Deformable Templates

Previous work Barcode reading has been studied and optimized for decades and it now represents a well established industrial standard. Independent likelihood maximization over each digit segment produces a sequence of symbols. Initially the system operates on light search state, searching for a barcode. Transform at ion After detecting the orientation, the whole image is rotated in reverse direction by the angle determined above.

After locating the peaks and valleys, the minimum bar width can be determined 10 from the distances between consecutive peaks and valleys. From the moment that the frame is received from the camera and the analysis begins, our algorithm takes an average of — ms for both localization and decoding.

We employ a particular form of deformable template matching, that produces robust results transfoem in difficult situations, such as the one shown in Fig. Commercial laser-based, hand-held barcode scanners achieve robust reading with a reasonable price tag.

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However, in the case of a high resolution image of a barcode at a short distance, producing a scanline width of pixels, cells are generated. Finally the generated barcode waveforms are decoded by recognizing peak locations.

Barcodes that are 4 cm wide can be decoded from up to 45 cm. At the two ends of the barcode there is a sequence of two bars separated by a space, all of unitary module width lateral guard bars.

Taking into consideration of the fact that image processing methods are consuming a large amount of processing time, we propose a method of controlling the real-time performance effectively.

If, due to noise or blur, a few pixels have intermediate gray values as in Fig. Processor Reaver for the Real-Time Implementation. The value k of a symbol is encoded by the sequence of module widths r 1 kr 2 kr 3 kr 4 k of the bars and spaces in the digit segment.

Reading 1-D Barcodes with Mobile Phones Using Deformable Templates

The barcodes printed on commercial products have sizes typically ranging from 2. Table 1 shows the possible stripe to stripe combinations with the normalized distances. For example, when the scanline width is equal to pixels, then 25 cells are generated. We then define a likelihood function to measure how well a deformed shifted and scaled template explains the observed intensity. Then, for each cell, one only needs to add together selected samples from the two sequences.

In general, these dedicated devices produce a high quality signal that allows for robust barcode reading.

Next, we show how to compute the terms in this sum. First barcode region given by the localizer is transformed into Hough Det ect Parallel Lines plane. We argue that this early-commitment operation translates into unrecoverable information loss, which makes the reader susceptible to noise, blur, and low resolution.

The resulting rectangular segment black squarealong with the selected scanline l nthe intersection points i L and i Rand the endpoints o L and o R. The second step requires that the scanline intersect all the bars, from the left to the right guard bars; since we extract an horizontal line, this becomes harder as the angle increases too much. Using Bayes rule, this is equivalent to. Determining the location of industrial bar-codes using neural networks Image Processing and Its Applications, Sixth International Conference Indeed, a number of cellphone apps have appeared that provide access via barcode reading to the full characteristics of and user reviews for a product found at a store.

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In our implementation we assume a localization tolerance in either end point equal to twice the width of the narrowest bar. The localization algorithm assumes that the cellphone is held in an approximately vertical fashion.

Keeping localization and decoding distinct allows for a higher computational efficiency. We then compute the total likelihood p k I by averaging p hoygh I ow over such density:.

Robust recognition of 1-D barcodes using camera phones. Although the algorithm relies on the assumption that the user is holding the cellphone so that the bars are vertical in the image, it produces good results even when the phone is rotated at an angle.

Commercial scanners, such as those used in supermarkets, shine a stripe of pulsed light on the code and measure sfotware intensity of its reflection; the use of active illumination makes them virtually insensitive to changes of ambient illumination. IV-B describes the Symbian implementation of the algorithm. As mentioned in readre real-time implementation, this does NOT suggest that the frame rate is constant at 4 fps.