What is Barcode Reading, and How Can AI Help With Barcode Detection?

In today's world, barcodes are everywhere. They are printed in ads, etched on practically every component of your vehicle's engine, and even printed on the side of your soda bottle and the labels of your soup cans.

We usually use barcode scanners to read barcodes. These scanners see the barcode through the software, transfer it to the screen, and perform the user action. So, how does this happen?

This article will discuss a barcode detection system and how it can work more effectively with AI.

What Are Barcode and Barcode Reading?

As the name implies, barcode reading is the process of barcode detection and identification. Barcode identification is performed through both software and hardware. But before we talk about the barcode detection system, let's get to know the barcodes.

Barcodes

A barcode is a pattern that machines can read and is often affixed to items, packaging, or individual components. It includes information that may be utilized for marketing and informative reasons, and it can also be used to monitor items during their entire lifespan.

The technology of barcodes was first developed in 1952, but it wasn't until 1974 that the first barcode was scanned on a box of Wrigley's gum. The barcode was located on the back of the product.

There are thousands of different barcodes, which may be broken down into two primary categories: 

●  One-dimensional (1-D) barcodes are those that only store information in the horizontal direction and have a character capacity of up to 25.

●  Two-dimensional (2-D) matrix codes can store substantially more information—up to 2,000 characters—than their one-dimensional (1-D) counterparts. These codes carry data in both vertical and horizontal directions.

Selection of 1-D and 2-D code formats

Laser scanners have been the standard method of "reading" one-dimensional barcodes. A sensor is used to collect light intensity that is reflected back from the barcode, which allows for the differentiation between white and black bars. This technique works by having a laser beam strike a revolving prism, which then focuses the laser beam onto the barcode.

This technique of scanning suffers from several shortcomings. Laser scanners rely on mechanical components such as revolving or oscillating mirrors or prisms, which are prone to wear and tear and are readily broken when stressed or vibration.

In addition, they cannot read two-dimensional codes, which are becoming more common in commercial and consumer applications, spanning from aerospace and automotive to food and pharmaceuticals, among other areas.

How do laser-based barcode readers work?

Vision-enabled barcode scanners are quickly becoming the technology of choice across a wide variety of business sectors for two primary reasons:

Since no moving components exist, they are perfect for use in applications subject to high wear and tear levels.

They are also capable of reading two-dimensional codes, making them perfect for sectors that need the storage of specific information on the product.

Since we have given all the details about barcodes, we can talk about how the detection system works and how more effective barcode detection can be made with AI.

Barcode Detection with AI

In various commercial applications, such as manufacturing, healthcare, and advertising, barcodes are used to embed essential information for product identification, expiry date, batch number, and other purposes.

While 1D barcodes have the appearance of long black stripes, QR codes are two-dimensional and have the appearance of black rectangles. The prevalence of barcodes in our contemporary society has led to the development several reliable and effective methods for reading barcodes. These methods include laser scanners and camera readers.

However, a few restrictions preclude them from being employed for applications on a wide scale. For example, laser scanners can only read one-dimensional (1D) barcodes.

Since barcode readers depend on light's reflection, they can also not scan barcodes shown on displays. Therefore, there is a pressing need to investigate alternative techniques of barcode recognition that are both dependable and flexible.

Thanks to AI, both faster and more accurate barcode detection can be possible. Machine learning does not cause any business disruption by reading barcodes that classical algorithms cannot read, even in low light.‍

How Does Barcode Detection Work?

The first approaches for detecting barcodes mainly relied on conventional signal processing techniques such as ;

●  Corner detection,

●  Gradient methods

●  Morphological operations, and soon.

These algorithms were generally evaluated using two standard barcode datasets, notably the Muenster Barcode Dataset (WWU) and the Artelab 1D Medium barcode database. Both of these datasets are maintained by Muenster University. These databases provide many barcodes, both 1D and 2D, with associated annotations.

More recently, due to the expanding popularity of artificial neural networks and deep learning, developers have quickly applied such approaches to the field of barcode detection. 

In the paper, the authors initially utilized the YOLO (You Only Look Once) detector to identify the bounding boxes of barcodes. After that, they supplied the detections as inputs to another neural network to predict the barcode's orientation.

After making the necessary adjustments to the orientation of the detected barcode, they passed the result into an open-source barcode reader to read the contents. By using ANN techniques, the developers could attain state-of-the-art performance and create new baselines on the datasets provided by WWU and Artelab.

Another machine learning-based barcode detection algorithm is SSD (Single Shot Detector).

To propose bounding boxes, SSD does not depend on region proposal networks, a technique that is used by other state-of-the-art methods such as Faster RCNN. Instead, SSD makes predictions about bounding boxes and classes in a single pass by drawing directly from feature maps.

SSD implements methods such as employing tiny convolution filters to forecast object classes and predicting the offsets to the preset bounding boxes to compensate for the decreased accuracy resulting from its use. SSD can still make real-time inferences even without the region proposal network while maintaining a sufficient performance level.

As a Barcode Reader Application: Cameralyze

Developing technology continues to offer all its convenience to users as it develops. While classical methods are left behind, AI-based systems that can be customized in all operations come to the fore. Barcode detection is no different.

It is now possible for users to make their own barcode reading applications through applications.

Cameralyze is one of these AI-based applications. One of the most basic features that distinguish cameralyze from its competitors is its accuracy rate (98.64%) and speed (4x faster than other services) in the services it offers.

With Cameralyze barcode detection solution, users can scan multiple codes, images, videos, or live streams up to 4x faster than special devices in harsh light oranges, even damaged tags. Cameralyze allows users to get the highest accuracy rate with state-of-the-art Barcode Scanner Solutions.

Users can easily integrate Cameralyze into their systems without technical knowledge or workforce because Cameralyze is a no-code platform. Also, it is free to start. Thus, users who want to try can create an account in the system and test the application for free.
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To Sum Up

Barcodes are an indispensable part of our lives and in all the products we use, so we will continue to see barcodes everywhere unless there is a significant innovation. However, the systems we use to read them are evolving rapidly.

Artificial intelligence allows barcodes to be detected much faster in low light and at different angles. With machine learning, deep learning, and other AI-based algorithms, all object detection solutions become the best over time. Let's see what technology will show us in the future.

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