2D Barcodes in Document Processing Applications: A Study of Their Effectiveness
The majority of papers that are going to be handled electronically include barcodes that store essential information that can be retrieved using barcode decoding software. When selecting a barcode symbology, a variety of factors should be taken into consideration. The most important differentiating feature to examine is whether a linear (1D) or a two-dimensional (2D) symbology will be used in the final design process. In 1D symbologies, as the name suggests, bars and spaces with varying width modulations are used to encode the information provided by the end user. When it comes to 1D symbols, the vertical dimension contains no meaning or information at all. The data density of 2D symbologies is much greater than that of 1D symbologies, owing to the fact that they encode information in both dimensions of the symbol. 2D symbols are usually represented by a regular grid of potential cell locations, where each cell may be either black or white, depending on the context.
Specifically, the usage of 2D symbols in document processing applications will be discussed in detail, due to the substantial data density advantage that 2D symbols have over one-dimensional (1D) symbols. The relative advantages of three prominent public domain 2D symbologies, including the Data Matrix, QR Code, and Micro QR Code, will be discussed in more detail below. In the next section, we will provide a short description of each symbology and then compare them based on their data density, error correction, and relative processing performance.
Data Matrix is an abbreviation for Data Matrix.
Data Matrix symbols are made up of a regular array of square cells with sizes ranging from a 10 by 10 grid up to a 144 by 144 grid, depending on the symbol. A one-cell silent zone is needed to surround the whole symbol in order to prevent distractions. In addition, rectangular sizes are available upon request. For each sign, a fixed “L” pattern is utilized for finding, and a clock track runs along the opposite sides of the “L” to aid in the calculation of the time. Additional clock tracks for bigger data matrices are available on the internal clock track bus. These fixed sites do not contain any information that may be decoded. They are there to help the decode software recognize the symbol as a Data Matrix and to assist in the identification of the symbol. The remaining grid positions are filled with either black or white squares, depending on the kind of information to be encoded at the grid location.
QR Code (Quick Response Code)
QR Code symbols also make use of a regular array of square cells with sizes ranging from a 21 by 21 grid up to a 177 by 177 grid, with the smallest cell size being 21 and the largest being 177. It is necessary to create a silent zone of four cells around the whole sign. The QR Code symbols include three finder patterns in each of their four corners, which aids in their discovery. In addition, there are internal alignment patterns, clock patterns, as well as format information on bigger symbols that indicates the code’s size in terms of bits per symbol.
A derivative form of the QR Code known as the Micro QR Code, is available for data applications that need fewer quantities of data. The Micro QR Code can encode up to 35 numeric digits in less space than the QR Code it is derived from. In all, it comes in four distinct square sizes: 11 x 11; 13 x 13; 15 x 15; and 17 x 17. Each size necessitates the creation of a 2-cell silent zone around the whole of the symbol. In addition to containing just one finder pattern, it also includes only a limited amount of clock pattern and format information.
Data Density and Error Correction are two important concepts in statistics.
The Data Matrix outperforms the QR Code in terms of data density by a wide margin. This is particularly true for those that provide lesser quantities of data. That’s because it has fewer fixed cell sites than the other networks. It does not provide any format information and does not spend as much attention on finding patterns as it could. The Micro QR Code was created to solve the problem of data density, and its size is similar to that of the Data Matrix for this particular data content.
When there is damage to the symbol or imaging problems, all three kinds of symbols utilize Reed Solomon error correction to identify and fix the mistakes. This is determined by the number of additional error-correcting codewords that are included in the symbol in addition to and beyond the codewords that were used to encode the data.
The data capacity of a symbol of a particular size is a function of the amount of error correction overhead as well as the quantity of data contained inside it. User-selectable error correction is not available in the Data Matrix since it utilizes a preset degree of correction. The amount of error correction codeword overhead varies depending on the symbol size, ranging from 62.5 percent for the smallest symbol to 28 percent for bigger symbols. The QR Code, on the other hand, offers four distinct degrees of mistake correction, each of which has an estimated recovery capacity of 7 percent, 15 percent, 25 percent, or 30 percent, depending on the level of error correction. The Micro QR Code offers a variety of options for the degree of error correction that may be used for each of the four permissible sizes. In contrast, the smallest provides just mistake detection, while the biggest enables up to a 25% increase in recovery capacity.
The quantity and kind of information provided by the user will determine the size of the symbol that is required. In addition to this, for QR Code and Micro QR Code, the quantity of error correction that is utilized will have an impact on how large a code will be. The following table shows the relative sizes and error-correcting capabilities of the three symbols shown in the previous section.
Relative Size (with Quiet Zone)/Error Correction Overhead (percentage)/Maximum Correctable errors are the symbols used to represent the data.
In the data matrix, the values are as follows: 1.00/583.0/3
QR Code — 3.70/65.3/8 — 3.70/65.3/8
Micro QR Code — 1.33/50.0/1 (Micro QR Code)
The degree of error correction that is utilized in QR Code and Micro QR Code is determined by the application that is being used. In cases when size is a concern, it may be tempting to minimize the amount of error correction overhead that is implemented. Depending on whether the barcode is broken or if the imaging environment makes it more difficult to get “perfect” pictures, this may result in a reduction in overall read rates for the symbol. In certain cases, barcodes on soft packaging may be destroyed because the symbol is curved. In other cases, barcodes on glossy tape can be damaged because the symbol can generate a specular reflection back to the camera. In general, if space allows it, it is preferable to use the full permitted error-correcting capacity in order to achieve the highest possible read rates.
Processing Speed in Relative Terms
Real-time applications, in which the time it takes to decode an image is critical, need a comparison of different symbologies based on how fast they can be decoded. Finding the symbol for a barcode in a big and cluttered picture is usually the most time-consuming aspect of decoding a barcode in that image. The more distinctive the finder pattern inside a barcode sign, the simpler it is to identify within an image that contains many other patterns. This lowers the amount of time spent processing. On the other hand, if a barcode symbology does not include a unique finder pattern, more time will be spent searching for the barcode symbol.
Because of the unique finding patterns included inside the symbols, QR Code and Micro QR Code offer a major advantage over Data Matrix. The QR Code is the most advantageous of the three options since it contains three finder patterns, each of which may be used to locate the symbol. The Data Matrix is characterized by the use of the “L” finder pattern and fixed clock lines. Unfortunately, they are not very distinctive patterns when it comes to forms that include a large number of text fields surrounded by boxes. Additionally, both QR (Version 7 and above) and Micro QR Codes include format information inside the symbol, which allows you to determine the size of the symbol and ensure that you are scanning a legitimate symbol. It is just a clock track on the opposite side of the symbol from the “L” corner that is provided by Data Matrix, since it does not include specific format data.
After scanning the busy form at 200 DPI, a single instance of the three barcode symbols was placed in the picture, with each symbol taking up 25 mil cells in each of the three symbols. Each symbol was decoded using VoloTM, a barcode decoding software toolkit from Omniplanar® that was utilized in three different runs to accomplish this. Only one symbology type was enabled for each pass throughout the process. According to the timetable below, Volo took about one hour and forty minutes to provide the decoded result and fully analyze the picture. Compared with data matrix decoding, decoding of QR Codes and Micro QR Codes was 3 to 4 times quicker. Almost exclusively as a result of the excellent finder pattern in the QR and Micro QR symbols, this has occurred.
Issue Time (msecs)/Total Time (msecs) — Symbolology (msecs)
30.8/74.5 is the data matrix.
QR Code — 7.2 x 23.4 pixels
micro-QR Code — 7.6 x 21.9 pixels
One must take into consideration data density, error correction, and processing time when choosing which 2D symbology type to employ in document-related applications. The Micro QR code as well as the Data Matrix are excellent options for situations where it is necessary to keep the symbol’s size as small as possible. Because of their superior finding patterns, QR Code and Micro QR Code are both preferable to Data Matrix when processing speed is of the utmost significance. The Micro QR Code is the ideal option for applications where both symbol size and processing speed are critical considerations, such as in healthcare. The biggest conceivable Micro QR Code, on the other hand, can only contain 35 numeric digits with the least amount of error correction (maximum of 3 errors). When using the highest degree of error correction, the data capacity is reduced to 21 numeric digits (maximum of 7 errors).
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