The Evolution of Barcode Scanning in Logistics Automation


Barcodes have played a pivotal role in revolutionizing supply chains since the 1970s. Traditional LED and laser scanners have been the go-to solution for reading barcodes, but with advancements in technology, new possibilities have emerged.

Here, I explore the limitations of traditional scanners and the rise of camera-based barcode scanners empowered by image analysis systems. I will delve into the intricate operations performed by these scanners and their superior efficiency in barcode location and decoding. Additionally, I will discuss the ongoing research in computer vision-based barcode reading techniques and the broader impact of machine vision in logistics beyond barcode scanning.

The limitations of traditional scanners

Traditional barcode readers operate by shining LED or laser light across a barcode, with the reflected beam detected by a photoelectric cell. While simple and effective in their time, these scanners have certain limitations that hinder their performance and restrict their application range. They require prior knowledge of barcode location, struggle with complex scenes, and are unable to read multiple barcodes simultaneously. Moreover, low-quality barcodes pose challenges, potentially leading to losses in time, money, and reputation.

The rise of camera-based barcode scanners

Camera-based barcode scanners, empowered by image analysis systems, have emerged as a game-changer in logistics automation. These scanners perform intricate operations, starting with image acquisition and preprocessing. Images are converted to grayscale, noise is reduced, and barcode edges are enhanced using various filters. Binarization is then applied, isolating black and white pixels for decoding. Unlike traditional scanners, image-based barcode scanners excel in barcode location and decoding. They eliminate the need for prior knowledge of barcode position and can locate and extract multiple barcodes in a single image.

The advantages of optical barcode scanners

As technology progresses, optical barcode scanners are gradually replacing LED and laser-based solutions, offering superior efficiency and performance. Computer vision-based barcode reading techniques have sparked extensive research, addressing challenges in both location and decoding steps. Barcode localization, the most intricate part, involves detecting and extracting barcodes accurately despite illumination variations, rotation, perspective distortion, or camera focus issues. Researchers continually refine barcode extraction techniques, using mathematical morphology and additional preprocessing steps for precise recognition.

Beyond barcode scanning: the impact of machine vision in logistic

The impact of machine vision in logistics extends beyond barcode scanning. Robot-operated warehouses, such as those employed by Amazon, rely on 2D barcodes to navigate shelves efficiently. Drones equipped with computer vision capabilities open new possibilities for delivery services, enabling autonomous and accurate package handling.

Machine vision technology is revolutionizing the way logistics operations are conducted, enhancing efficiency, accuracy, and overall customer experience.

QR Codes for Business vs Industry

Example of a QR code for business use

In a previous post I discussed the different types of bar codes. Aside from the 1D bar codes that we see in the grocery store, the most common type of bar code today is the QR code.

The QR code was 1st designed for the automotive industry to track vehicles in the assembly process. The QR code system became popular outside the automotive industry due to its greater storage capacity compared to standard UPC bar codes. A QR code can have up to 7,089 ASCII characters and can read numeric, alphanumeric, byte/binary, and kanji. Businesses often use this type of QR code on vehicles and products for advertising. When a picture is taken with a cell phone, typically in a QR code reader app, the user will be taken to a website for more information.

Sharpshooter vision sensor for reading micro & QR codes
Sharpshooter vision sensor for reading micro & QR codes

Micro QR codes, on the other hand, have a limitation of 35 digits of numeric characters. These are usually seen in industrial applications. For example, they are seen on cam shafts, crankshafts, pistons, and circuit boards. An example of data that is often written to a micro QR code would be a serial number to track and trace through an assembly plant. An industrial vision sensor is typically needed to decipher micro QR codes.

An example of a QR code (left) vs a micro QR code (right)

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Isn’t a bar code just a bar code?

Bar codes are normally read via a red line laser scanner, or a camera with decoding and positioning software.

There are 3 main types of bar codes.

1D (one dimensional), 2D (two dimensional) and a different type of 2D code is QR (Quick response) codes that we use today.

Each code has a little different attribute and how it’s read.

 1D bar codes are the ladder line bar codes you typically see in a grocery store, on merchandise and packaging.

While there are many different types of 1D bar codes and how they decipher a code the appearance is typically like the picture below.







A 2D Data Matrix code is much smaller than a 1D and can hold quite a bit more information. They can actually hold up to 2,335 alphanumeric characters.

There is redundancy built into the code, in case the code is scratched or defaced.

The code below is an example of a 2D Data Matrix code.


The code is read by utilizing a camera and decoding / positioning software.

A QR Code can hold more information than a Data Matrix code.

It can decipher numeric, alphanumeric, byte/binary and kanji.

While it was 1st developed for the automotive industry tracking parts during vehicle manufacturing, it is typically linked to a website when the code is scanned with a camera in a cell phone.

An example of the QR Code is pictured below.

QR Code

The code is read by utilizing a camera and decoding / positioning software.

There are various types of vision sensors that can be used to read different types of bar codes. You can learn more on Balluff’s website at