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Barcodes and AI: The Future of Barcode Scanning

Barcodes and AI: The Future of Barcode Scanning

Barcode scanning is essential to many industrial activities – from product scanning and inventory tracking to biometric checking. Companies are looking for innovative strategies to enhance barcode usage efficiency, such as integrating artificial intelligence.   

AI can allow businesses to boost scanning speed, accuracy, and automation. Advanced industrial scanners can do inventory management, product tracking, and barcode detection.  

This post delves into what the future holds for AI-powered barcode technologies.  

AI for Accurate Barcode Scanning  

AI and machine learning algorithms can enable a barcode scanner to analyse datasets. It will help the scanner detect data patterns, representing quality barcodes. As a result, the scanner can utilise the pattern information to read damaged barcodes.  

Further, AI-powered systems can analyse barcodes against poor lighting and skewed angles. Unlike traditional barcode scanners that rely on neatly printed labels, these systems can adapt to label discrepancies.  

Moreover, machine learning algorithms help detect barcode errors like missing digits or incorrect letters. It improves the scanning accuracy and overall efficiency of the system. So, organisations will aim to invest more in these advanced AI tools and software to improve scanning. 

Efficient Barcode Detection with Deep Learning  

Mobile or general-purpose scanners must detect the barcode before scanning it. Numerous barcode symbologies can be difficult for standard scanners to recognise. This causes scanning delays and inefficient detections.  

This is where deep learning algorithms come into play. Training these learning models with sample images helps them produce bounding boxes as outputs. These results will have locations of possible barcodes in the scanned image. The process will be faster, more accurate and efficient.  

Then, non-maximum suppression allows the algorithms to remove redundant detections.  

Convolutional Neural Networks for Barcode Recognition    

After systematic scanning and recognition, barcode scanners determine which symbology the detected barcode belongs to. This might be difficult for traditional scanners. So, industries are adopting convolutional neural networks (CNN) for these cases. They train these algorithms with datasets of labelled barcode images.  

CNNs help companies recognise unique patterns in the code to match them with the appropriate symbology. Adjusting the contrast and image noise offers improved results. Here are the other benefits of the process –  

  • Helps recognise and process barcodes in low resolution and poor lighting  
  • Efficient recognition of different barcode formats, such as QR codes and ID barcodes 

Thus, the CNN algorithms can also help detect and process damaged barcodes. It aids industries in fetching vital product information from damaged containers or machines.  

The Impact of AI on Barcode Scanning In The Healthcare Domain 

AI-based barcode systems and algorithms allow medical professionals to process multiple patient samples. The scanning consistency and accuracy are vital here, as a misread barcode might lead to incorrect test results. Also, accurate scanning and recognition help modern barcode systems prevent the wastage of tissue specimens. 

So, hospitals and medical research centres are keen on implementing artificial intelligence to improve their processes. 

Machine learning algorithms might help speed up identifying patient records, medical equipment, and drugs. This massively impacts administration and patient care, decreasing the chances of misidentifying patients.  

Moreover, they can help the medical staff with patient data entry and improve inventory management. All this elevates organisational processes and creates a reliable environment for offering better services.  

The Bottom Line  

Due to the proven benefits of AI-based barcode scanning, companies will optimise financial strategies to invest in AI. They might need to hire better teams and accommodate technical infrastructure to execute the changes.  

The associated devices must be top-quality if you need to improve barcode scanning processes. 

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