How AI Can Help with Barcode Reader

What is Barcode Reader?

In the digitizing world, barcodes are everywhere. It is widely used from the barcode that the cashier reads to determine the price of the products in the grocery shopping you make daily to the barcodes produced by the companies for the products.

A barcode reader is an optical scanner that can read printed barcodes, decode the data in the barcode, and transmit the data to a computer. It, too, is made up of a light source, a lens, and a light sensor for converting optical impulses into electrical signals. Furthermore, nearly all barcode readers include decoder circuitry that can analyze the sensor's barcode image data and send the barcode's content to the scanner's output port. In other saying, barcode readers are sensors that identify goods and materials in manufacturing and logistics.

They accomplish this by detecting barcodes that meet several standards and then supplying the bar code IDs to a superior system. The primary goal of using these devices in automated applications is to achieve the highest possible reading quality: When barcode readers detect labels, the quality with which they do so varies. This quality can be expressed as a percentage. The percentage represents the amount of contrast detected.The label is no longer read if the value falls below a certain threshold.

System operators face difficulty locating bar code readers as quickly as possible when they are no longer providing adequate reading quality and determining why. Without additional data on possible sources of error, this can be a time-consuming task. In large systems, such as intralogistics, which have up to 2,000 bar code readers and kilometer-long transport routes: When in doubt, a technician, while operating under time constraints, must trace the entire path of a shipping item to identify a misaligned sensor or obstructive factors in its immediate vicinity. In the case of borderline, such as when the bar code reader is partially aligned and reads most of the time successfully but fails to detect labels occasionally, aggravating the situation.

This can be because the barcode reader is slightly inclined or only reads in the border area, or it could be due to other factors, such as low-quality labels. 

Factors that Influence the Reading Quality 

For serialized tracking, modern supply chains, warehouse management systems, and the traceability of both food and pharmaceuticals rely on barcodes. The readability of a barcode is determined by the qualities of the substrate being printed on as well as the quality of the printer and ink combination. Moreover, using the barcode reader to generate corresponding data to find the causes of errors is only possible in certain circumstances. Ensure that the sensors monitor their status and, if necessary, transfer data to the superior system via OPC UA. However, let's take a look at the following factors that influence barcode readability: 

Low contrast: A barcode, as mentioned above, encodes information in a different pattern of alternating signs. When there is insufficient contrast between the light and dark parts of these alternating marks, it will make it difficult for the barcode reader to distinguish between the two and result in an unreadable error. 

Reflectivity: Because of protective films applied to cardboard, consumer-facing packaging is frequently shiny. This increased reflectivity can interfere with the contrast between the barcode and the printing surface, resulting in a no-read. 

Indistinctions: Printing errors can cause consistent readability issues. These errors could include insufficient printing of the code's darker areas, fuzzy edges, or low-resolution printing that results in less-than-crisp barcodes.

Some industries prohibit highly reflective packaging and require barcodes to be printed on a white background to ensure high contrast and barcode readability. In some countries or regional markets, this may be required by law or government regulations.

Artificial Intelligence in Barcode Detection 

Using Artificial Intelligence (AI) can be very beneficial when identifying barcodes on goods. Interfering factors can be quickly and easily identified during system commissioning and operation. Let's take a closer look at why AI is beneficial.

AI can assist in distinguishing between various causes and, as a result, identifying the causes of interferences or poor reading quality. Another advantage of using AI is that barcode readers work as usual without generating additional work for the customer when working with large data volumes.

For example, Cameralyze AI-based Barcode Reader responds to every request with the same speed, regardless of the size of the data set you are working with. In addition, many labels pass through barcode readers and are read at various installation sites. The Cameralyze AI-based barcode reader solution can scan up 4x faster than dedicated devices in challenging light or angles, even damaged labels, across multiple codes, on image, video, or live stream. 

Regarding quality percentage, each station and label achieves a different result. AI solves this complex equation system and answers whether poor reading quality always occurs with a specific barcode reader, only with one label or label type, or always at a specific installation location. 

Machine Learning in Barcode Detection 

AI-based solutions offer some recommendation algorithms, and with these algorithms, you can easily come up with solutions to problems. These are the same methods streaming services use to evaluate user behavior and recommend corresponding films or series based on this analysis. In this user behavior analogy, bar codes represent films, and bar code readers represent streaming service users. The recommendation algorithm recommends that a label is rated as more or less "attractive" for different bar code readers. Thus, with a certain percentage, it is possible to determine which sensor or label is "unattractive," i.e., borderline or noticeably problematic.

Implementing the AI-Based Solution on Edge Devices 

This type of AI-based solution can be implemented using edge devices or the cloud, depending on the needs of the customer and the system. An edge device is a separate device located near a sensor group and gathers, analyzes, and transmits data from the sensor group. Multiple edge devices can be linked together. A barcode reader can also pass on this information and report that there is a problem because an edge device can communicate two-way, gather and evaluate data, and send the analysis back to the sensors. This benefit is that no changes to the customer's IT architecture are required. If data from multiple locations is merged, the solution can also be run in the cloud. 

To Sum Up

As can be understood from the topics mentioned above with the barcode reader, barcodes are integral to our daily life and take place in the whole chain from producer to consumer. I would like to emphasize once again the advantage of using AI-based solutions, from the difficulty of reading barcodes to the need for a workforce.

Also, the Cameralyze Barcode Reader solution allows you to scan up to 4x faster than dedicated devices in challenging light or at angles, even damaged labels, across multiple codes, on an image, video, or live stream and monitor real-time metrics. Get the highest accuracy rate with the Cameralyze state-of-art Barcode Reader Solutions. Click here to create your barcode reader application in minutes with Cameralyze no-code platform. 

‍

‍

‍