Pervasive 2D Barcodes for Camera Phone Applications

In a previous study, we evaluated six 2D barcodes using eight criteria for standardization potential: omnidirectional symbol reading, support for low-resolution cameras, reading robustness under different lighting conditions, barcode reading distance, error correction capability, security, support for multiple character sets, and data capacity. We also considered the fidelity of the camera phone\u27s captured image as a metric for gauging reading reliability. Here, we review the six 2D barcodes and then use an additional metric - a first-read rate - to quantitatively verify our earlier results and better gauge reading reliability

2D-barcode for mobile devices

2D-barcodes were designed to carry significantly more data than its 1D counterpart. These codes are often used in industrial information tagging applications where high data capacity, mobility, and data robustness are required. Wireless mobile devices such as camera phones and Portable Digital Assistants (PDAs) have evolved from just a mobile voice communication device to what is now a mobile multimedia computing platform. Recent integration of these two mobile technologies has sparked some interesting applications where 2D-barcodes work as visual tags and/or information source and camera phones performs image processing tasks on the device itself. One of such applications is hyperlink establishment. The 2D symbol captured by a camera phone is decoded by the software installed in the phone. Then the web site indicated by the data encoded in a symbol is automatically accessed and shown in the display of the camera phone. Nonetheless, this new mobile applications area is still at its infancy. Each proposed mobile 2D-barcode application has its own choice of code, but no standard exists nor is there any study done on what are the criteria for setting a standard 2D-barcode for mobile phones. This study intends to address this void. The first phase of the study is qualitative examination. In order to select a best standard 2D-barcode, firstly, features desirable for a standard 2D-barcode that is optimized for the mobile phone platform are identified. The second step is to establish the criteria based on the features identified. These features are based on the operating limitations and attributes of camera phones in general use today. All published and accessible 2D-barcodes are thoroughly examined in terms of criteria set for the selection of a best 2D-barcode for camera phone applications. In the second phase, the 2D-barcodes that have higher potential to be chosen as a standard code are experimentally examined against the three criteria: light condition, distance, whether or not a 2D-barcode supports VGA resolution. Each sample 2D-barcode is captured by a camera phone with VGA resolution and the outcome is tested using an image analysis tool written in the scientific language called MATLAB. The outcome of this study is the selection of the most suitable 2D-barcode for applications where mobile devices such as camera phones are utilized

Decoding Different Patterns in Various Grey Tones Incorporated in the QR Code

Using colors in bar codes causes errors that may adversely affect their readability (Tan etal. 2010), given that the contrast between data and background modules is reduced. Due to the unreliability of using color bar codes, most designers still keep to the limitations placed by Pira International (Smithers Pira) in 2002 (Williams, 2004). Since the contrast between data modules and background modules is the most important aspect in the process of reliable bar code decoding, this paper explores the dependence of reliable decoding of QR codes incorporated with combinations of grey tones on the technical characteristics of the cameras on smartphones that were marketed in the period between 2008 and 2012

Distance transform and template matching based methods for localization of barcodes and QR codes

Visual codes play an important role in automatic identification, which became an inseparable part of industrial processes. Thanks to the revolution of smartphones and telecommunication, it also becomes more and more popular in everyday life, containing embedded web addresses or other small informative texts. While barcode reading is straightforward in images having optimal parameters (fo cus, illumination, code orientation, and position), localization of code regions is still challenging in many scenarios. Every setup has its own characteristics, there fore many approaches are justifiable. Industrial applications are likely to have more fixed parameters like illumination, camera type and code size, and processing speed and accuracy are the most important requirements. In everyday use, like with smart phone cameras, a wide variety of code types, sizes, noise levels and blurring can be observed, but the processing speed is often not crucial, and the image acquisition process can be repeated in order for successful detection. In this paper, we address this problem with two novel methods for localization of 1D barcodes based on template matching and distance transformation, and a third method for QR codes. Our proposed approaches can simultaneously localize sev eral different types of codes. We compare the effectiveness of the proposed methods with several approaches from the literature using public databases and a large set of synthetic images as a benchmark. The evaluation shows that the proposed methods are efficient, having 84.3% Jaccard accuracy, superior to other approaches. One of the presented approaches is an improvement on our previous work. Our template matching based method is computationally more complex, however, it can be adapted to specific code types producing high accuracy. The other method uses distance transformation, which is fast and gives rough regions of interests that can contain valid visual code candidates

Information management system using 2D barcodes and cell phone technology

One of the challenging problems of pervasive computing is to link a physical object with digital information because many of the pervasive computing applications require manual inputs or complex image processing to obtain information related to a real object. The use of 2D barcodes eliminates such excess processing to acquire the needed information. The 2D barcodes have high capacity to store data, ’ are less prone to human input error and act as a tool to acquire information on site without network access. The currently available solutions use 1D barcodes to represent dynamic information residing in a database and use 2D barcodes to represent only static information that also encode only URLs. In all such applications, ’ the source of information gets restricted to either a database or the static data encoded inside a 2D barcode. None of such solutions takes advantage of 2D barcode capabilities to collect information from different sources and attach it to the real world entity. Moreover, ’ a 2D barcode can also represent and categorize complex text information. Our approach integrates the capabilities of 1D barcode into 2D barcode to represent and classify the complex digital information collected from different sources. We design and implement an information management system on a handheld device that has image processing and barcode decoding capabilities to address the above- –mentioned problem. Our prototype provides a generic framework to decode either 1D or 2D barcode, ’ parse the complex information (both dynamic and static) inside the 2D barcode, ’ differentiate the complex information based on content types and classify the image based on the barcode format. It also assists users in decision- –making and information analysis. An example system application can be deployed in grocery stores as a part of the enterprise information management system

Universal Barcode Detector via Semantic Segmentation

Barcodes are used in many commercial applications, thus fast and robust reading is important. There are many different types of barcodes, some of them look similar while others are completely different. In this paper we introduce new fast and robust deep learning detector based on semantic segmentation approach. It is capable of detecting barcodes of any type simultaneously both in the document scans and in the wild by means of a single model. The detector achieves state-of-the-art results on the ArTe-Lab 1D Medium Barcode Dataset with detection rate 0.995. Moreover, developed detector can deal with more complicated object shapes like very long but narrow or very small barcodes. The proposed approach can also identify types of detected barcodes and performs at real-time speed on CPU environment being much faster than previous state-of-the-art approaches