Thanks to machine vision, products can be reliably identified throughout the entire flow of goods. Corresponding characters or codes can be difficult to read due to a variety of circumstances. Deep-learning-based technologies offer valuable assistance in this regard. Using the MVTec HALCON machine vision software, the company Visione Artificiale has developed a robust application for the traceability of aluminum bottles during production.
Based in Bione, northern Italy, Visione Artificiale SRL specializes in the integration of industrial image processing technologies (machine vision) in end-to-end automated robotics systems. With more than 20 years of industry experience and in-depth machine vision expertise, the company develops solutions for a wide range of industrial applications. These include systems for the quality control of components, high-precision measurement for the automation of inline inspection processes, 3D vision systems, bin-picking, deep-learning-based applications, and many other solutions.
On behalf of a company in the food industry, Visione Artificiale developed an application to automate the traceability of CO₂-filled aluminum bottles for carbonating still water. Various pieces of information, such as serial numbers, product data, filling date, and a logo are laser-engraved on the surface of the cylindrical bottles. Thanks to optical character recognition (OCR), the application uses these combinations of letters and numbers to identify the aluminum bottles automatically and with a high degree of precision. This is vital when it comes to ensuring smooth production processes, the quality of the bottles delivered, and thus customer satisfaction. To ensure complete traceability of the containers, machine vision is used to check the accuracy of the engraved information. Automated inspection using machine vision is not only robust and fast, it can also be carried out 24/7. This helps the company reduce costs over the long term.
Reflections and specks impair identification with OCR
The material into which the text is engraved presents a challenge. Due to the surface texture of aluminum, various reflections and specks may occur as a result of lighting during image acquisition. These make it difficult to segment the characters correctly, thus severely disrupting the OCR-based identification process. To ensure robust recognition rates, Visione Artificiale relies on Deep OCR technology, a feature that is integrated into MVTec HALCON, the comprehensive standard machine vision software. HALCON is a product of MVTec Software GmbH, a company based in Munich. Deep OCR technology is based on deep-learning algorithms and is able to localize characters regardless of their orientation, font type, and polarity. In addition, letters can be grouped automatically, which makes it possible to identify entire words. Moreover, since Deep OCR completely avoids the misinterpretation of characters with similar appearance, it significantly boosts recognition performance. HALCON’s Deep OCR has been trained for the reliable identification of a wide variety of fonts.
Automating and accelerating the control process
As part of the application, the cylindrical aluminum bottles are each locked in spindles and rotated. A line scan camera scans the bottle and captures a two-dimensional image of its curved surface. The first step is to locate areas in the image that contain letters and numbers. The network does that by determining bounding boxes with a confidence score that indicates the likelihood that those boxes contain text. The network then determines the characters contained within the boxes so you can check and verify the accuracy of the information they contain. This automates and accelerates the entire control process. The setup is equipped with two cameras and rotating mechanisms, making it possible to test two bottles simultaneously per cycle. This makes the throughput even faster while also increasing efficiency.
“Due to the special properties of the material, a conventional OCR system would not have been able to identify the engraved text. To achieve robust recognition rates despite the reflections, we needed an intelligent OCR system that can rise to this challenge. Deep OCR has proven to be the optimal solution for our requirements. Thanks to comprehensively pretrained deep learning networks, Deep OCR can recognize even difficult-to-read text with high accuracy. MVTec’s HALCON library offers an impressive range of deep learning algorithms that enable us to successfully accomplish this complex task,” confirms Fazio Saverio, founder and owner of Visione Artificiale. During the implementation, Saverio and his team were accompanied by the company iMAGE S. iMAGE S supports its customers in all aspects of machine vision and also provides its own products and technologies.
Higher productivity and quality thanks to Deep OCR
The use of the HALCON machine vision software, including Deep OCR, made it possible to trace the CO2 bottles using serial numbers in the first place. After all, it is only the automated monitoring and verification of the engraved text that allows this process to be carried out cost-effectively and at the required speed. In addition, the employees who would otherwise have to check the character codes manually are relieved of this monotonous job and can focus on more demanding tasks. Ultimately, optimized traceability increases the productivity of the entire process chain and takes the resulting product quality to an entirely new level.
