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Linear barcode scanning system based on dynamic template matching for OOF blurred images

April 2015

April 2015
2015:418-421

Authors:

Vellore Institute of Technology

A Novel Linear Barcode scanning system based on a Dynamic template matching scheme. The proposed system works entirely in the Spatial domain and is capable of reading Linear Barcodes from low-resolution Images containing severe OOF blur. This paper treats Linear barcode scanning under the perspective of deformed Binary waveform analysis and classification. A directed graphical model is designed to characterize the relationship between the blurred barcode waveform and its corresponding symbol value at any specific blur level. A Dynamic programming-based inference algorithm is designed to retrieve the optimal state sequence, enabling real-time decoding on Mobile devices of limited processing power.

Block diagram of proposed system DYNAMIC TEMPLATE MATCHING Dynamic Template Matching (DTM) which can efficiently find the optimal state variable sequence and, therefore, the barcode value. Merits of the Proposed Systems are as .(i)It gives the capability to deal with OOF level.(ii)Its capability of real-time processing, which is made possible by directed graphical model.(iii)Similar to OOF blur any blur can able to handle by proposed techniques.(iv)Directed graphical model will help to generate closer scan line of original barcodes. MODULE DESCRIPTION The work presented in this paper consists of the following major modules such as  Barcode localization  Linear barcode scan line segmentation and observation sequence modelling  Standard reference waveform segments generation  A directed graphical model  Dynamic template matching RESULTS Figure 2 & 3 shows the Clear & Blurred Input Images .Figure 4 & 5 shows the Gray scale conversion of Clear & Blurred Input Images respectively .Figure 6 & 7 shows the Gradient Images .Figure 8 & 9 shows the Gaussian Motion Filtered Noise. Figure 10 & 11 is the Binarized Barcode. Figure 12 & 13 is Deformed Scanline.Figure 14 & 15 is Observation sequence. Figure 16 & 17 is Standard Deviation Waveform. FUTURE ENHANCEMENT This paper mainly addresses the analysis and classification of binary waveforms under OOF blur in the form of linear barcode scanning, the methodology presented can be extended to other Blurs such as moving Blur and other related domains, such as Document Analysis & Recognition, where Image blurs are also challenging issues and Deblurring is normally resorted for Character Segmentation and Word Recognition

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INTERNATIONAL CONFERENCE ON RECENT ADVANCEMENT IN MECHANICAL ENGINEERING &TECHNOLOGY

(ICRAMET’ 15)

Journal of Chemical and Pharmaceutical Sciences ISSN: 0974-2115

JCHPS Special Issue 9: April 2015 www.jchps.com Page 418

LINEAR BARCODE SCANNING SYSTEM BASED ON DYNAMIC TEMPLATE

MATCHING FOR OOF BLURRED IMAGES

D.Vijendra Babu, S.Shalini, P.T.Sarath, Enugula Niharika, Ismail Shurab

ECE Department, Aarupadai Veedu Institute of Technology, Vinayaka Missions University, Paiyanoor-603

104.Chennai.Tamil Nadu.

ABSTRACT

A Novel Linear Barcode scanning system based on a Dynamic template matching scheme. The proposed

system works entirely in the Spatial domain and is capable of reading Linear Barcodes from low-resolution Images

containing severe OOF blur. This paper treats Linear barcode scanning under the perspective of deformed Binary

waveform analysis and classification. A directed graphical model is designed to characterize the relationship

between the blurred barcode waveform and its corresponding symbol value at any specific blur level. A Dynamic

programming-based inference algorithm is designed to retrieve the optimal state sequence, enabling real-time

decoding on Mobile devices of limited processing power.

Key Words: Barcode, Dynamic Template Matching, Blurred Images

INTRODUCTION

Digital Image refers to processing of a 2 Dimensional picture by a Digital Computer. In a broader context,

it implies Digital processing of any two dimensional data. A Digital Image is an array of real or complex numbers

represented by a finite number of bits. An Image given in the form of a transparency, slide, photograph or an X-ray

is first digitized and stored as a matrix of binary digits in computer memory. This digitized image can then be

processed and/or displayed on a high-resolution television monitor. An Image Processor does the functions of

Image Acquisition, Storage, Pre-processing, Segmentation, Representation, Recognition and Interpretation and

finally displays or records the resulting image. Digital Image Processing has a broad spectrum of applications, such

as Remote Sensing, Medical Processing, RADAR, SONAR & Acoustic Image Processing, Robotics & Automated

inspection of Industrial parts.

This paper mainly addresses the analysis and classification of Binary waveforms under Out of Focus

(OOF) Blur in the form of Linear Barcode scanning, the methodology presented can be extended to other blurs

such as moving Blur and other related domains, such as Document Analysis and Recognition, where Image Blurs

are also challenging issues and Deblurring is normally resorted for Character Segmentation and Word Recognition

BARCODE

Barcode Technology has found its applications in many industries and has been playing an important part

in people’s daily lives. Multiple generations of barcode scanning systems ranging from earlier LASER Scanners to

more recent area Charge Coupled Device (CCD) scanners have been invented and developed. As the location/size

information of bars and spaces is of paramount importance for deciphering information embedded in barcodes,

modern barcode scanning systems generally request well-focused barcode signals, which help in the retrieval of

location/size-related features by confining the edge interaction between the code patterns. Depth-of-field (DOF),

the range of distance at which the scanned symbol is sufficiently in focus to be read without error, is an important

aspect of any specific barcode scanning system. Area CCD scanners have the advantage of reading both linear and

2D barcodes, but have less DOF than that of laser scanners, because the directional and coherent nature of laser

light permits expanded DOF. This DOF constraint has limited the availability of area CCD scanners on various

occasions. For example, linear barcode scanning based services are largely not available on mobile devices with

fixed-focus lenses because the barcode images captured by these devices contain excess edge interactions triggered

by out-of-focus (OOF) blur, which cannot be handled by current techniques.

Unfortunately, images taken by cell phone cameras are often of low quality. Many cell phone cameras on

the market are equipped with low-grade lenses, generally lacking focusing capability, which often produce blurred

images. Few cell phones have a flash and, therefore, motion blur and noise can be expected with low ambient light.

All of these factors, possibly combined with low image resolution, make barcode reading difficult in certain

situations. Indeed, all existing image-based barcode readers have limited performance when it comes to images

taken in difficult light conditions, or when the camera is not close enough to the barcode. In order to improve

accuracy, barcode reading apps usually prompt the user to precisely position the camera to ensure that the barcode

covers as much of the frame as possible. This operation can be somewhat bothersome, as it requires a certain

amount of interaction with the user, who needs to frame the barcode correctly using the viewfinder.

EXISTING SYSTEM

Existing system requires that the Barcode has been localized with fairly good precision. This operation is

facilitated by the fact that a Barcode is bordered to the side by a white area whose size is prescribed by the

standard. We propose a simple and fast algorithm for localization that assumes that the bars are approximately

INTERNATIONAL CONFERENCE ON RECENT ADVANCEMENT IN MECHANICAL ENGINEERING &TECHNOLOGY

(ICRAMET’ 15)

Journal of Chemical and Pharmaceutical Sciences ISSN: 0974-2115

JCHPS Special Issue 9: April 2015 www.jchps.com Page 419

vertical. The localization algorithm has no pretense of optimality but works reasonably well in existing method.

The disadvantages of the Existing methods are (i)OOF blur cannot be handled by current techniques,(ii)not feasible

for real-time barcode scanning in real-world situations,(iii)Template matching scheme only matches the scan lines

which are not affected by maximum noise & (iv) Its capability of real-time processing, which is made quite

impossible because sometimes resolution low images may need to find.

PROPOSED SYSTEM

The proposed approach is different in the aspects that it neither exploits Blur invariant features because

none of the currently popular features are robust to severe OOF blur, nor tries to reconstruct OOF free barcode

signal from the blurred images because Image Reconstruction is usually a mathematically ill-defined problem,

computationally difficult to be dealt with. One notable feature of the proposed system is that its Template Matching

scheme takes Image Blur and interactions of character templates into consideration by modelling the waveform of

Barcode characters and their interactions at any specific Blur level. This gives the proposed system the capability to

deal with severe OOF level. The maximum OOF blur level that can be handled by the current implementation of

the proposed system is up to seven times of the X-dimension of a Barcode. Another feature of the proposed system

is its capability of Real Time processing, which is needed in practical situations. This feature is made possible by

designing a directed graphical model, which not only establishes the relationship between the blurred barcode

waveform and its corresponding symbol value

Figure.1.Block diagram of proposed system

DYNAMIC TEMPLATE MATCHING

Dynamic Template Matching (DTM) which can efficiently find the optimal state variable sequence and,

therefore, the barcode value. Merits of the Proposed Systems are as .(i)It gives the capability to deal with OOF

level.(ii)Its capability of real-time processing, which is made possible by directed graphical model.(iii)Similar to

OOF blur any blur can able to handle by proposed techniques.(iv)Directed graphical model will help to generate

closer scan line of original barcodes.

MODULE DESCRIPTION

The work presented in this paper consists of the following major modules such as

 Barcode localization

 Linear barcode scan line segmentation and observation sequence modelling

 Standard reference waveform segments generation

 A directed graphical model

 Dynamic template matching

RESULTS

Figure 2 & 3 shows the Clear & Blurred Input Images .Figure 4 & 5 shows the Gray scale conversion of

Clear & Blurred Input Images respectively .Figure 6 & 7 shows the Gradient Images .Figure 8 & 9 shows the

Gaussian Motion Filtered Noise. Figure 10 & 11 is the Binarized Barcode. Figure 12 & 13 is Deformed

Scanline.Figure 14 & 15 is Observation sequence. Figure 16 & 17 is Standard Deviation Waveform.

FUTURE ENHANCEMENT

This paper mainly addresses the analysis and classification of binary waveforms under OOF blur in the

form of linear barcode scanning, the methodology presented can be extended to other Blurs such as moving Blur

and other related domains, such as Document Analysis & Recognition, where Image blurs are also challenging

issues and Deblurring is normally resorted for Character Segmentation and Word Recognition

INTERNATIONAL CONFERENCE ON RECENT ADVANCEMENT IN MECHANICAL ENGINEERING &TECHNOLOGY

(ICRAMET’ 15)

Journal of Chemical and Pharmaceutical Sciences ISSN: 0974-2115

JCHPS Special Issue 9: April 2015 www.jchps.com Page 420

Figure 1

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INTERNATIONAL CONFERENCE ON RECENT ADVANCEMENT IN MECHANICAL ENGINEERING &TECHNOLOGY

(ICRAMET’ 15)

Journal of Chemical and Pharmaceutical Sciences ISSN: 0974-2115

JCHPS Special Issue 9: April 2015 www.jchps.com Page 421

Figure11

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Acoust Speech Signal Process

In this paper, we present a novel approach to detection of one dimensional bar code images. Our algorithm is particularly designed to recognize bar codes, where the image may be of low resolution, low quality or suffer from substantial blurring, de-focusing, non-uniform illumination, noise and color saturation. The algorithm is accurate, fast, scalable and can be easily adjusted to search for a valid result within a specified time constraint. Our algorithm is particularly useful for real-time recognition of bar codes in portable hand-held devices with limited processing capability, such as mobile phones.

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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.

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Using total variation-based energy minimization we address the recovery of a blurred (convoluted) one dimensional (1D) bar code. We consider functionals defined over all possible bar codes with fidelity to a convoluted signal of a bar code and regularized by total variation. Our fidelity terms consist of the $L^2$ distance either directly to the measured signal or preceded by deconvolution. Key length scales and parameters are the $X$-dimension of the underlying bar code, the size of the supports of the convolution and deconvolution kernels, and the fidelity parameter. For all functionals, we establish parameter regimes (sufficient conditions) wherein the underlying bar code is the unique minimizer. We also present some numerical experiments suggesting that these sufficient conditions are not optimal and the energy methods are quite robust for significant blurring.

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The ubiquitous supermarket checkout scanner is indeed a well engineered and effective device. There is, nevertheless, demand for better devices. Existing scanners rely on simple and indeed low-cost signal processing to interpret bar code signals. These methods, nevertheless, fundamentally limit label reading and cannot be extended. A new method based on the deterministic EM algorithm is described. First results show a substantial improvement in label reading depth of field, which is an important performance parameter for bar code readers

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Bar code reconstruction involves recovering a clean signal from an observed one that is corrupted by convolution with a kernel and additive noise. The precise form of the convolution kernel is also unknown, making reconstruction harder than in the case of standard deblurring. On the other hand, bar codes are functions that have a very special form—this makes reconstruction feasible. We develop and analyse a total variation based variational model for the solution of this problem. This new technique models systematically the interaction of neighbouring bars in the bar code under convolution with a kernel, as well as the estimation of the unknown parameters of the kernel from global information contained in the observed signal.

Linear barcode scanning system based on dynamic template matching for OOF blurred images

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