Optical Character Verification, or OCV, is a technology that verifies the accuracy and quality of printed text on manufactured items. Manufacturers and sellers check the accuracy of information on packages, from food labels to medication packaging, in a fast and automated way using OCV.

OCV works with Optical Character Recognition (OCR) to read and verify text. Reading text with machines used to rely on pattern recognition, pixel matching, and special fonts, but now, thanks to recent AI advances, any text font is easily readable.

OCV has many use cases in the consumer packaged goods (CPG) sector. It helps manufacturers maintain consistent quality and accuracy in their labeling process. They can make sure they meet regulatory requirements and protect consumer safety. 

OCV can be used to verify that barcodes, batch numbers, expiration dates, and other information are printed correctly and accurately, which helps maintain traceability and makes managing recalls easier. 

In this article, we’ll dive into this topic and learn how OCV works and its applications. Let’s get right to it!

Understanding Optical Character Verification (OCV)

To understand OCV and its capabilities better, we'll start by briefly touching on OCR. While OCV serves a specific purpose, it often relies on OCR.

Optical Character Recognition is the process of converting images of text, like scanned documents or photos, into digital text files that can be manipulated (edited or searched). It can analyze and identify individual characters and translate them into their digital form. 

Optical Character Verification then compares the translated digital data against its reference data to make sure it’s correct. In the case of consumer packaged goods, OCV is used to check the accuracy of printed information like expiry dates and lot codes. It is also useful for quality control-related applications in which the expected data is known beforehand. 

Benefits of OCV in Consumer Packaged Goods

Let’s take a look at some more key benefits of OCV in the CPG industry.

Quality Assurance

The system uses cameras and software to check the text and numbers printed on products, like expiration dates and batch codes. Compared to traditional means, it's extremely fast and reliable. But OCV doesn't just check for typos, it also ensures the font size, style, and even analyzes the way the numbers are spaced. It helps prevent mistakes that could lead to products being recalled or posing a safety risk.  

Regulatory Compliance

Making sure the packaging is labeled properly and accurately is an important part of regulatory compliance, and OCV systems are used here for efficiency. OCV systems identify the content and format of the labels and check their correctness with the data in its database. It also checks the details of mandatory information on the labels, like the list of ingredients, allergen warnings, and nutritional information. These systems help manufacturers comply with complex labeling regulations that vary by region. OCV technology saves lots of time and manual labor.

Brand Protection

OCV helps fight counterfeiting by checking unique codes and security features on the packaging, making sure you're getting the real product. OCV also safeguards the overall image of the brand, making sure fonts, logos, and colors on the packaging are consistent and exactly as the brand intended. Consumers can trust that the information they see on the packaging is accurate and reflects the brand they know and love.

Operational Efficiency

By automating tedious manual checks for errors, OCV makes inspections much faster. This means factories can send out products quicker and produce more overall. Plus, OCV helps save money because there's less of a need for human inspectors. OCV can also catch mistakes early on, so there's less wasted packaging material due to printing errors. It's a win-win for both speed and savings.

How Optical Character Verification Works

OCV works hand in hand with an OCR engine. A simple OCR engine works by storing many different font and text image patterns as templates. It uses pattern-matching algorithms of OCV to compare text images, character by character, to its internal database. If the system matches the text word by word and is exactly what the system is looking for, it’s verified. Let's see all the steps involved in the verification process:

• Image Capture: High-tech cameras capture sharp images of the packaging at different stages of production. Both area scan and line scan cameras are used for this purpose. Proper lighting is also key to getting a good picture.

• Image Preprocessing: Once the picture is taken, OCR cleans it up a bit. It might resize it, remove any smudges, or adjust the contrast to make the text stand out more. 
• Character Detection: In this stage, the OCR is used to detect and recognize all sorts of fonts and sizes. The OCR system uses trained machine learning models that can segment and recognize characters. It analyzes the clean image and figures out each individual letter and number on the packaging.

Verification: Then the OCV system checks the letters and numbers it found against a reference, like a pre-defined list of expiry dates. Any mismatch, like a missing number or wrong date, gets flagged as an error.

• Discrepancy Flagging and Alerts: If the system finds an error, it triggers an alert, like a flashing light or a loud beep, alerting the factory workers to investigate the problem.
• Real-Time Feedback: Because OCV catches errors early on, the factory can fix them quickly. Fixes might involve adjusting printing settings or even stopping the production line to prevent mistakes from happening again. It's all about keeping things running smoothly and creating high-quality products.

Now that we have learned what OCV is and how it works. Let's explore its applications in the Consumer Packaged Goods industry.

Applications of OCV in CPG

Food and beverage companies need to track their products from factory to shelf. That's why they print codes like expiration dates and batch numbers on everything from cereal boxes to juice bottles. 

These codes help identify when and where a product was made, making it easier to control quality. The codes often combine pictures and plain text, and that's where OCV comes in.  OCR can detect the text part of the code, while OCV checks to make sure the entire code, picture, and text together is printed correctly. 

Expiry Date Verification: Preventing Spoiled Products

OCV takes high-quality pictures of expiry dates on food packaging.  Then, it compares those characters to a list of correct expiry dates for each product. If anything doesn't match, OCV raises a red flag. This alerts factory workers to investigate further (it can also be automated using robotics) and potentially even remove those products from circulation to make sure no expired food reaches you.

Batch Number Verification: Streamlining Product Tracking

Similar to expiry date verification, the OCV system takes images of batch numbers and verifies them by comparing them with a list of valid batch numbers within the system’s database. By reinforcing accurate batch number tracking, OCV facilitates swift product identification and retrieval in case of recalls or quality concerns.

Barcode Verification: Ensuring Smooth Product Flow

Unlike text recognition, OCV doesn't need character recognition for barcode detection. Instead, it uses special algorithms to check if the barcode itself is built correctly, like having all the right parts. If the barcode is wrong or messed up, OCV raises a flag so it can be reprinted. This prevents problems at checkout counters and keeps products flowing smoothly through warehouses and stores. 

Challenges Using OCV

Achieving accuracy in diverse conditions is a major challenge for these systems. OCV needs to be efficient in all kinds of factory settings.  Fluctuations in lighting, shadows, or even dust particles on packaging can make it hard to get a clear picture. Blurring images might lead to mistakes when reading the text. OCV systems can avoid this issue by using special lighting setups.

Another challenge is real-time verification without delays. The system needs to verify information in real-time without slowing down production lines. Slowing down the conveyor belt for detection can result in overall inefficiency. To avoid this, OCV systems are designed for efficiency.  They use AI acceleration and smart algorithms to analyze and verify images quickly, keeping the factory running smoothly.

Managing large-scale deployments and data processing is also a concern. Deploying OCV  systems across a large factory can be complex. Managing all the cameras for the system, handling the huge amount of data they generate, and processing it efficiently requires a robust infrastructure. A solution to this might involve powerful computer systems and specialized software solutions to handle all the information OCV gathers.

Aside from technical complications, label designs are always changing with trends. New designs, fonts, and printing methods pop up all the time.  OCV needs to be adaptable to these changes, which might involve training the system on new data sets. Alternatively, OCV systems might use flexible algorithms that can handle variations in packaging elements so they can keep up with the latest trends.

Conclusion

New technologies, like edge computing, make analyzing products using OCV directly on cameras seamless. Reducing latency speeds up inspections and smooths production lines. OCV also thrives when used with other quality control AI innovations. For example, computer vision techniques like object detection or instance segmentation can help check a product's position on a production line. With the help of AI, OCV is only getting better, leading to even higher-quality products.

Optical Character Verification, or OCV, is an essential tool that helps double-check the accuracy of information printed on product packaging. It uses advanced cameras and software to check for errors in things like expiration dates, batch numbers, and barcodes. OCV helps manufacturers maintain high quality, meet labeling rules, protect their brands, and run production lines more efficiently by catching mistakes early. As technology improves, OCV will become even better at keeping products safe and accurate for consumers.

Keep Exploring

Check out these resources to learn more:

• Article on What is OCR?
• Article on Best OCR Models for Text Recognition in Images.
• Article on How to Detect Text in Images with OCR
• Article on Using an OCR Model to Build a Computer Vision Application