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Logistics companies are actively pursuing automation to improve operational efficiency at industrial sites. Among them, the automation solutions attracting attention are for acquiring and processing image information about the shape of the shipment, the size of the shipment, damage, and loss, etc. 
Upgrade your logistics system by using Neurocle software to inspect shipment appearance and components, minimizing damage and defects. 

Component inspection

Missing item detection & completeness check

It is necessary to determine if there are any missing components in the finished product as well as judging the integrity of the product.

Neurocle software helps reduce product defects by identifying the number of products and identifying missing items.

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Company 'K' component inspection

Missing components

Product code classification

Product code reading & classification

Neurocle software can read the unique ID or code of the received product. This allows you to build logistics automation solutions that send goods to the appropriate zones.


Company 'L' serial number inspection

Product code classification

Manufacture date inspection

Production & expiration date OCR inspection

Inspection of the production date entails many difficulties. Due to high-speed printing, it is difficult to maintain the printing quality and letter size and spacing of bottles, pouches, and embossed/engraved products. 


Neurocle software can recognize production and expiration dates engraved on the surface of products such as bottles and pouches, maintaining their accuracy based on training results from a large amount of alphabetic and numeric data.


Company 'P' manufacture date inspection

Manufacture date

Box damage inspection

Box appearance defect inspection

A damaged box may leave a bad impression on consumers, or even negatively impact the inner contents.
Neurocle software can prevent this issue by identifying external damage on boxes during inspection.

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Company 'P' logistic damage inspection

Box damage

Innovate product quality inspection with deep learning.

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