Machine vision is utilized to perform Optical Character Recognition (OCR). This information is sent to the cloud to create an inspection report, allowing plant operators to easily monitor facility inspection through mobile devices.
SolVision’s superior recognition capabilities ensures that OCR of bicycles can be accurately carried out regardless of how the identification numbers look or light refraction levels.
Solvison enables OCR for license plates and container numbers even when shapes, color and locations of characters vary, and are pictured in poor lighting conditions.
With SolVision OCR identification numbers can be analyzed and converted into numerical data for database logging and connecting with automobile identification numbers in real-time
Inner wheels are rubber rings that each have a corresponding serial number to help identify its model specifications and matching tire size.
Overcoming traditional AOI limitations, Solomon’s SolVision quickly performs OCR without being affected by background or lighting conditions, complexity, or appearance of the serial number.
Using AI deep learning, Solomon SolVision’s Instance Segmentation tool can learn the many types of text and number defects on labels, even if they are irregular or subtle.
Solomon SolVision’s Feature Detection tool first learns the position of the tray, then performs OCR with more flexibility than AOI systems.
Bridging machine vision and robotics, SolVision uses AI image analysis to perform OCR, which converts information from traditional machines into legible digital data that can be immediately processed.
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