Key Takeaways
Here are the advantages of using advanced AI-based OCR technology for challenging code reading applications:
- Pre-trained efficiency: Omni font libraries reduce lengthy training, working out-of-the-box for most applications.
- Industry applications: Automotive, electronics, and life sciences benefit from accurate code reading under tough conditions.
- Quick retraining: Engineers can retrain on misread characters directly on the factory floor.
- Handles challenges: Reads low-contrast, deformed, and embossed codes on many surfaces.
- Boosts productivity: Reduces no-reads, maximizes machine uptime, and ensures compliance with traceability standards.
Using AI for Optical Character Recognition (OCR)
Unlike traditional machine vision systems, which perform well on clear and simple fonts against uniform backgrounds, deep learning OCR tools excel in handling deformed, skewed, or low-contrast fonts. They shine where traditional systems struggle, making them ideal for manufacturers dealing with complex backgrounds or unpredictable application-specific fonts.
Let’s explore how deep learning-based software offers an easy, accurate alternative to these complex OCR/OCV inspection applications.
Deep learning OCR for the automotive industry
Automotive manufacturers and parts suppliers use serial numbers to track high-value parts through the supply chain and make sure they are matched with the correct assemblies. Many parts contain ten-digit, dot-peened serial numbers so that when errors occur during testing, the source can be traced.
In the case of a recall, affected parts can be quickly taken off the market. However, these codes often become deformed during processes like sanding and casting, causing challenges for conventional OCR. For example, the glaring metallic surfaces can sometimes confuse the camera in an automated inspection system. When dot-peened serial numbers become grossly deformed and illegible, this slows down the OCR and OCV processes and threatens effective traceability.
Deep learning OCR tools overcome this by using pre-trained libraries that accurately identify even the most deformed characters. Training an omnifont library is quick and doesn't require an advanced vision expert. Cognex Deep Learning only requires application-specific adjustments. Instead of training an algorithm to identify each code’s individual character or number, a training engineer—not a vision expert—simply defines the region of interest (normally the area with the misread characters), sets the character size, and labels the image. Any misread characters or application-specific fonts can be easily retrained on the factory floor.
Deep learning OCR for the electronics industry
Laser-etched codes on electronic components like integrated circuit (IC) packages and lead frames are essential features for all electronic hardware manufacturers. These barcodes and serial numbers contain information about when and where parts were manufactured, their lot numbers, and testing data. They may also encode information about solder temperature and flux density—essential information as components are mounted onto chips and assembled into modules.
These codes are read at every stage of value-add up through final assembly and device testing to ensure that hardware is assembled correctly and contains the right components. Given the small size of most semiconductors and the space constraints on a PCB board, a manufacturers’ ID system must be robust to keep production running at full speed and keep track of high-value components. This is true for finished device manufacturers, who routinely must read laser-engraved codes on the sides of slider heads which can be as small as 1.1 mm x 1.4 mm. Laser-marked codes can degrade during production and become difficult to read.
In these situations, deep learning-based OCR/OCV technology offers an out-of-the-box solution, skipping over hours of training thanks to a pre-trained, omnifont library. Productivity benefits are immediate as no-reads decrease and machine uptime is maximized.
Deep learning OCR for the packaging industry
Manufacturers must have systems in place to recognize and verify the information trail which follows each packaged good through the supply chain. To ensure full package traceability, food and beverage and consumer products manufacturers confront challenging codes.
The usual suspects are low-contrast characters printed on label-based packaging codes or deformed, embossed characters on injection-molded parts, such as a bottle cap. The codes may be used to match multi-part packaging or, more likely, contain date/lot codes that embed information about contents, origin, and date of manufacture. In these cases, manufacturers rely on OCR/OCV equipment to quickly locate products and pull them out of production or off shelves. Traditional OCR/OCV technology requires upfront training to learn various fonts and can struggle to decode poorly contrasted characters.
Deep learning-based OCR/OCV technology is able to read most poorly contrasted letters and numbers automatically. By embracing deep learning-based technology, manufacturers can make sure food is kept up-to-date, comply with traceability laws, and facilitate recalls with little production impact.
Deep learning OCR for the life sciences industry
Effective OCR and OCV is important for the life sciences industry, which has tightly regulated track-and-trace laws. To remain in compliance, manufacturers and hospitals need to scan the codes on medical devices, surgical tools, and patients’ hospital ID bracelets at every point of handling and point of use. This allows them to keep a tight hand on devices and pharmaceuticals throughout the supply chain in case of a safety event.
With so much equipment and products to manage, codes can get deformed and skewed. Imaging quality may also be subpar, changing the code’s appearance to the camera. Rather than investing time to train a machine vision system to recognize fonts, the life sciences industry is turning to deep learning-based image analysis software.
Deep learning-based OCR tools are effective and easy to deploy on codes printed onto challenging substrates and prone to deformity like dot-peened codes on metal parts, embossed characters on injection-molded products, label-based codes on packaging, and laser-etched codes on electronic components. Cognex Deep Learning's OCR/OCV technology recognizes most alphanumeric text out-of-the-box and only requires brief upfront training to set the region of interest and character size. The system can retrain misread characters quickly on the factory floor, so that manufacturers don’t miss a beat.
To find out how deep learning is revolutionizing factory automation applications like OCR and OCV, download the free whitepaper, AI for Factory Automation.