Proceedings VCM 2012 53 phát hiện và nhận dạng mã vạch một chiều từ hình ảnh

Tuyển tập công trình Hội nghị Cơ điện tử toàn quốc lần thứ 6 393 Mã bài: 96 A vision based method for 1D barcode detection and recognition Phát hiện và nhận dạng mã vạch một chiều từ hình ảnh Trần Thị Thanh Hải Viện nghiên cứu quốc tế MICA, HUST - CNRS/UMI - 2954 - INP Grenoble e-Mail: thanh-hai.tran@mica.edu.vn Abstract: While traditional methods for barcode reading use specific devices (e.g. laser scanners) which are very disadvantageous because of their lack of mobility, reading barcodes from a camera phone is becoming an interesting and low-cost solution in this ubiquitous computing era. This paper presents a method for 1D barcode recognition from images. This method is composed of 2 main phases: barcode location and barcode decoding. Our contribution found in the barcode location phase where we combine Discrete Cosine Transform (DCT) based technique and scan-line based techniques to improve the location rate while reducing the computational times. For the barcode decoding, a statistical recognition is used. The experimental results show the good performance of our method in comparison with state of the art methods. Tóm tắt Hướng tiếp cận truyền thống để nhận dạng mã vạch thường sử dụng các thiết bị chuyên dụng như máy quét laser. Các thiết bị này thường gắn ở một vị trí cố định, khó di chuyển. Trong một số ứng dụng như tra cứu sản phẩm tại chỗ (ví dụ trong siêu thị, trên quảng cáo, v.v), hướng tiếp cận đọc mã vạch từ điện thoại di động là một giải pháp thú vị và ít tốn kém. Bài báo này trình bày một phương pháp nhận dạng mã vạch từ ảnh thu nhận từ camera của điện thoại di động. Phương pháp đề xuất gồm hai pha chính: định vị mã vạch và giải mã. Các đóng góp chính của chúng tôi là sử dụng kết hợp hai kỹ thuật quét dòng và phép biến đổi cosin rời rạc cho phép nâng cao độ chính xác định vị, đồng thời giảm thời gian tính toán. Các thử nghiệm cho thấy phương pháp đề xuất cho hiệu quả nhận dạng cao, hoàn toàn có khả năng tích hợp trên điện thoại di động cho các ứng dụng khác nhau như tra cứu sản phẩm trong siêu thị. 1. Introduction Nowadays, barcodes, 1D barcodes in particular, play an important role in the modern life. The need to use barcodes in different applications requires researches on barcode symbologies (codings) as well as barcode readers. In reality, there are a lot of devices for reading barcodes such as barcode pens, hand-held laser scanners, etc. Laser scanner is the most popular device that can be found in any marts / stores or documentation centers. However, the biggest disadvantage of this device is its lack of mobility because most of the time, it can only be used together with the corresponding Point of Sale machine. A current trend for barcode reading is to recognize 1D barcode from images captured by a camera because of its low-cost and mobile properties. However, the problem of visual barcode recognition is not simple. The difficulties come not only from mistakes during printing barcodes but also from imaging condition (e.g. shadow, reflection, dirty surface) that produces distorted, skew, dirty, too dark/bright barcode images. This paper presents a method for visual recognition of 1D barcodes while overcoming some of the above difficulties. Our framework consists of two main components: barcode location and barcode decoding. Our main contribution in this paper found in the barcode location phase where we combine Discrete Cosine Transform (DCT) based technique and scan-line based technique to improve the location rate while reducing the computational times. The paper is organized as follows. In the section II, we present and evaluate some related works. In the section III, we propose a framework for 1D barcode recognition and explain in more detail each component of the framework. Some experimental results will be presented in section IV. 2. Related works All methods for barcode recognition from images must deal with two main problems: barcode location and barcode decoding. In the literature, 394 Trần Thị Thanh Hải VCM2012 there exists numerous approaches for barcode localization using analysis of spatial domain [1], Gabor filtering [2], analysis of Wavelet domain [3], or DCT domain [4]. Based on the observation that a barcode is composed of parallel and adjacent bars and spaces, which are usually aligned horizontally, a barcode region should be dominated with vertical texture then a wavelet based method has been used to locate barcode regions in [3]. With wavelet transform, an image is divided into four subbands (one low-frequency subband and three high- frequency subbands). Using the characteristics of high-frequency wavelet subbands, some criteria based on energy of coefficients on the levels of subbands have been defined to locate barcode regions. Once a barcode region is located, edges of bars will be determined by finding the zero- crossings of the average intensity curve built from 8 parallel scan-lines. For barcode decoding, the nearest neighbor classification method is used to find out the most similar reference vector. This method has been tested with 292 EAN-13 barcode images taken by a NOKIA 7650 camera phone, shows a good performance of the method: the correct barcode location rate is 94.18% and the correct barcode recognition rate is 85.62%. However, this method works only under the following assumptions: barcodes must be placed as in front of the camera as possible, in the center of the camera view and the physical barcode’s size should be bigger than 3cm in length. DCT expresses a sequence of finitely many data points in terms of a sum of cosine functions oscillating at different frequencies. A barcode is composed of black and white stripes alternative which are aligned in i-direction, AC-coefficients of the DCT in i-direction are of high magnitude. As DCT coefficients of a barcode region not only can be distinguished from non-barcode region, they give also information about orientation of the barcode. These properties are explored in [4] to locate barcode regions in image. Following the authors, this method is very fast and gives good results in case where there are no high textured regions in the image than barcode regions. However, the paper did not resolve the problem of barcode recognition. Wavelet-based and DCT-based approaches allow locating barcode regions in images. However, these methods are sometime quite time-consuming. A simple but quite efficient technique that is widely used in the literature is scan-line-based [5], [6]. First, a scan-line passing the center of the image will be extracted. Then the curve built from the intensity of points lying on this scan-line will be smoothed using a Gaussian filter. The maxima / minima of this smoothed curve will be determined that help for finding dynamic thresholds for binarizing the curve. With the binary curve, the width of spaces and bars will be calculated. Barcode recognition is done by digit classification using similarity measure between the candidate and the references. About 1000 images of barcodes have been taken by a N95 camera phone. The recognition rate of 90.5% at 640x480 image resolution was achieved with assumptions that there is only one horizontal barcode at the center of each image. In summary, most of methods for barcode location are based on specific properties of barcode, which are the parallel and dense distribution of bars and spaces. DCT can locate barcode area in all directions while current scan-line based approach can only deal with horizontal barcode. The barcode recognition rate depends strongly of barcode location and binarization. Nowadays, barcode recognition has been developed as a commercial product and integrated in several mobile phone platforms such as Red laser [7], ShopSavvy [8], XZing [9]. The problems with these products are: 1) The algorithm developed in each product is not public so we cannot understand why it works and why not in a certain case; 2) As commented by some users, the algorithm does not work well in poor lighting. In this paper, we would like to present a public method that provides comparable performance. 3. Vision based 1D barcode recognition 3.1 Brief description of 1D barcodes In this paper, we are interested into 1D barcodes: UPC-A, EAN-13 or ISBN-13 because they are arguably the most widely used throughout the world to mark retail products that are scanned at points of sale. However, the framework that we propose can deal with all kinds of barcodes with a little modification in the algorithm because both barcode location and barcode recognition algorithms are based on the knowledge of barcode structures and their appearances. The Figure 1 shows an image of a EAN-13 barcode. The human readable data is written under the barcode i.e. the machine readable data. This barcode consists of 13 digits. The last digit is a checksum computed from the first 12 digits. The barcode starts with a left-hand guard bar (black- white-black) and ends with a right-hand guard bar (black-white-black). Between the guard bars, there are two blocks, each composed of 6 digits, separated by a center guard bar (white-black-white- black-white). Two different EAN-13 barcodes will Tuyển tập công trình Hội nghị Cơ điện tử toàn quốc lần thứ 6 395 Mã bài: 96 have the same amount of bars and spaces but the width of these bars and spaces will be different because they encode different numbers. These characteristics of barcode will be used in barcode location and recognition presented in the next sections. Figure 1. Structure of an EAN-13 barcode 3.2 Proposed framework for 1D barcode recognition Based on analysis on barcode structures and their appearances, we found that both DCT and scan- line based techniques are good for barcode location. For barcode decoding, a statistical method seems to be convenient. We propose therefore a framework for barcode location and decoding as in the Figure 2. The framework is composed of 3 principal modules: 1) localization of 1D barcode region in the image; 2) once the barcode region is located, it is extracted from the image and binarized; 3) the binarized barcode region will be decoded to output a sequence of digits (final result). In the following, we will explain in more detail each component of the whole 1D barcode recognition system. Figure 2. Framework of 1D barcode recognition system 1) Localization of Barcode region: This module consists of 2 components. First, we locate 1D barcode regions using DCT technique. As this localization is always inaccurate, we then apply the scanline based technique to re-locate it in order to find more accurate barcode boundaries (guard bars). a) DCT-based method for 1D barcode region localization: The algorithm of DCT based barcode location consists of 10 steps: 1. Divide the input image into 8x8 pixels blocks. 2. Apply DCT on each 8x8 pixels block. This step will produce for each block 64 coefficients in which the first coefficient DC- value represents the average value of the image block, 63 remaining AC-values represent spatial frequencies of image block in the ascending order. We set all DC-values of all image blocks to 0. 3. Calculate the average DCT-block from all blocks of 8x8 DCT-coefficients. 4. Group all DCT-coefficients ij c of one frequency range f of the average DTC block into an array f G , so that })1, ,1{)(}, ,1{)(,  fifjfjficG ijf . For example, },,,,{ 23133332313 cccccG  . Then calculate the largest DCT-coefficient )max( max ff Gc  from each frequency range f in the average block. The coefficients maxf c indicate the coefficients in the barcode area that are strongest. 5. Compute a weight matrix W of dimension 8x8 where each elements w ij is defined as follow:       elsek ccifk w d fije ij , , max Where k d and k e are the emphasis and desemphasis factors, respectively. 6. Perform an element multiplication of each 8x8 DCT block with W. Then calculate the sum of each DCT block. The higher the sum of the DCT-block, the higher is the likelihood that it belongs to barcode regions. The DCT-sums make up a subsampled DCT image by a factor 8 in each dimension. We set 0 to negative values and scale positive values to the range [0, 255] to create a gray-scale image. 7. Perform morphological closing on the gray- scale image obtained from step 6 to smooth the barcode region. 8. Convert gray-scale data into binary using Otsu thresholding technique. 9. Look for 8-connected components. 10. Choose rectangular connected regions as candidates of barcode region. b) Scanline-based method The DCT based algorithm gives an approximate location of the barcode region. We consider it as the region of interest for which we will apply the 396 Trần Thị Thanh Hải VCM2012 scan-line based technique to re-localize in a more accurate manner. The output of this phase is a region of barcode well localized with boundaries which are correctly determined to pass to the barcode decoding. 2) Barcode Decoding: Barcode decoding is carried out in the following steps as illustrated in the Figure 3. We can see in this figure two main phases: 1) learning digit classifiers and 2) barcode decoding given a new barcode area. a) Digit and barcode representation As analyzed above, bars and spaces can cover one to four modules of the same color. Each digit is composed of two bars and two spaces with a total width of 7 modules. We represent each digit by a vector of 4 elements corresponding to the widths of ordered bars and spaces. This vector will be normalized to unit magnitude so that it is invariant in case of scale change of barcode. Given a binary barcode region, we compute the widths of bars and spaces by counting the sum of black /white pixels. To reduce errors produced during binarization, first we compute average width of single module then the double, triples; quadruple modules widths will be calculated. Now, each barcode region is represented by a 12 elements-vector representing 12 digits. Each element, corresponding to a digit, is again a 4 elements-vector. Figure 3. Barcode decoding schema b) Statistical learning of digit classifiers The learning of digit classifiers is simple. To build a digit classifier, we take normalized samples of this digit from training dataset, compute the average one and considered it as the reference vector for this digit class. We do the same for 30 encoded digits (because left-hand digits are encoded into 2 sets A, B, and right hand digits are encoded in set C) to obtain 30 reference vectors. c) Barcode decoding For recognition, each digit will be compared to digits learnt during training phase. Left-hand digit will compared with 20 digits in A, B sets to choose the most similar one (Euclidian distance based measure). Similarly, right-hand digit will be compared with 10 digits in C set to choose the most similar one. This makes a hypothesis of the barcode. This hypothesis will be passed to the verification step of the checksum number. If it is correct, the hypothesis is confirmed. If not, we generate other hypothesis (by changing the meta- number) and verify until we obtain a valid code. 4. Preliminary results and discussions 4.1 Dataset preparation For learning and testing, a dataset needs to be prepared. Until now, the number of samples for training each digit class remains quite modest (we use only 10 images of barcode). For testing we use 100 images taken from different sources (http://cvpr.uni-muenster.de/research/barcode) or by ourselves. These images contain barcodes with different status: change in size, orientation, noise, dirty, reflections, etc. The image resolution is 640x480. 4.2 Experimental results The proposed method for barcode recognition will be evaluated based on 3 performance measures: 1) barcode localization rate; 2) barcode recognition rate; and 3) computational time. Barcode localization rate is the ratio between the number of located barcode regions and the total number of barcode regions in the ground truth. A barcode region is considered as located if the located region covers more than 80% the ground truth region. Barcode recognition rate is the ratio between the number of barcodes which are successfully decoded and the total number of barcodes in the ground truth. The computational time is the total time (in second) that the recognition system takes to read an input image; process it then output a localized barcode region and a digit sequence. The TABLE I shows performance evaluation of barcode recognition system using the combined technique for barcode localization. In all cases, we use the same recognition technique for barcode decoding. Tuyển tập công trình Hội nghị Cơ điện tử toàn quốc lần thứ 6 397 Mã bài: 96 TABLE I. PERFORMANCE EVALUATION OF BARCODE RECOGNITION METHODS Method Localizatio n rate (%) Recogniti on rate (%) Time (s) One scan-line 75 70 1.38 Multiple (6) scan-lines 80 80 2.28 DCT 80 75 1.54 DCT and scan-line 80 80 0.33 We found that the technique based on one scan-line gives worst results in term of barcode localization and recognition rate. When using multiple scan- lines, the noisy influence will be decreased, that improves the performance in both localization and recognition rate. However, the computational time will increase. Our proposed method (DCT combined with one scan-line technique) gives a better performance in localization rate than the case using multiple scan-lines. An additional advantage of this combined technique is that it is quite efficient in term of computational time. In the below figures, we show some examples of barcode localization using our combined method. We can see this method can deal with difficult situations such as barcode is un-planar (Figure 4), barcode is rumpled (Figure 5), or shadowed (Figure 6). In all of these figures, the green rectangles are the barcode regions located by the only DCT technique; the red ones are the barcode regions localized using the combined technique. Figure 4. Localization of unplanar barcode Figure 5. Localization of rumpled barcode Figure 6. Localization of shadowed barcode 5. Conclusion In this paper, we presented a framework for barcode recognition from images. The main contribution of our framework in comparison with the literature ones is we used DTC technique to locate barcode regions that is robust to orientation and size. The scan-line technique is used next to re- localize the barcode region so we obtain a more accurate location of barcode boundaries. This combined technique is better in barcode location and computational time for recognition. In the future, we would like to deploy this method on mobile phone platform to build an online assistance system for customers in stores. Acknowledgments This study was done in the framework of the International cooperation project 10/2011/HĐ- NĐT. References [1] D. Chai and F. Hock, Locating and Decoding EAN-13 Barcodes from Images Captured by Digital Cameras, in 2005 Fifth International Conference on Communications and Signal Processing,. 2005: Bangkok, Thailand. p. 1595 - 1599 [2] A.K. Jain and Y. Chen. Bar code localization using texture analysis in Proceedings of the Second International Conference on Document Analysis and Recognition. 1993. [3] R. Oktem, Barcode localization in wavelet domain by using binary morphology, in Proc. of IEEE SIU'04. 2004. p. 499-501. [4] A. Tropf and D. Chai, Locating 1-D Bar Codes in Dct-Domain, in Proceedings. 2006 IEEE International Conference on Acoustics, Speech and Signal Processing. 2006: Toulouse, France. [5] O. Gallo and R. Manduchi, Reading Challenging Barcodes with Cameras, in Proc. of IEEE Workshop on Applications of Computer Vision. 2009, 7. p. 1-6. [6] S. Wachenfeld, S. Terlunen, and X. Jiang, Robust 1-D Barcode Recognitionon Camera Phones and Mobile Product Information Display, in Lecture Notes in Computer Science, Springer-Verglas Berlin Heidelberg. p. 53-69. [7] http://redlaser.com/. 398 Trần Thị Thanh Hải VCM2012 [8] http://shopsavvy.com/. [9] http://code.google.com/p/zxing/. Bibliography Thi Thanh Hai TRAN graduated in Information Technology from Hanoi University of Science and Technology in 2001. She has followed MS degree in Imagery Vision and Robotic at Grenoble Institute of Technology in 2002. She received her Ph.D. degree from Grenoble Institute of Technology, France in 2006. She is currently lecturer/researcher at Computer Vision group, International Institute MICA, Hanoi University of Science and Technology. Her main research interests are visual object recognition, video understanding, and human-robot interaction. . v.v), hướng tiếp cận đọc mã vạch từ điện thoại di động là một giải pháp thú vị và ít tốn kém. Bài báo này trình bày một phương pháp nhận dạng mã vạch từ ảnh thu nhận từ camera của điện thoại. điện tử toàn quốc lần thứ 6 393 Mã bài: 96 A vision based method for 1D barcode detection and recognition Phát hiện và nhận dạng mã vạch một chiều từ hình ảnh Trần Thị Thanh Hải Viện nghiên. cận truyền thống để nhận dạng mã vạch thường sử dụng các thiết bị chuyên dụng như máy quét laser. Các thiết bị này thường gắn ở một vị trí cố định, khó di chuyển. Trong một số ứng dụng như tra