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Eldad Ben
by Eldad Ben Shalom on October 6, 2015
posted in Solutions & Applications
Untitled Document

Insights about OCR Vision Applications with CIJ Printers (Part 3 of 3)

This article is the third of three articles about OCR vision applications with continuous inkjet printers (CIJ). In this third article, you will learn about the different vision solutions possible with Microscan vision technology. AutoVISION Software incorporates many possible vision tools to choose from, including OCR algorithms with trainable characters, as well as vision tools that are simpler but can be highly effective in the inspection of CIJ print. So how do we know which strategy to follow? In some cases, simple pixel-based algorithms can solve many of the common printing problems of a CIJ printer setup. In some cases pixel-based tools are more effective than OCR tools. Later on I’m going to describe the machine vision concepts of each approach. I will also point out the advantages and disadvantages of each method. I will provide guidelines for when to use each strategy, and when one strategy may be more effective than another strategy. Once you have this knowledge you will be able to create powerful vision inspection systems for high-speed production lines with CIJ printers using simple components that allow for a rapid yet highly effective design process.

Below are listed various potential vision solutions that are used to solve inline inspection applications challenges of CIJ-printed characters at high speed. They are divided into three primary groups:

  • Group 1 – Pixel-Based Vision Tools: Vision algorithms performed on pixel values to find edges or to count pixels within a range of grey levels. Such vision tools are used for various vision inspections – not only for print inspections.
  • Group 2 – OCR tools with match data input, to compare the read text with a matching data which is the expected result.
  • Group 3 – OCR tools with custom pass/fail logic, which apply advanced logic in order to qualify/disqualify the inspected product.
by Stephanie Hatley on September 28, 2015
posted in Industry Trends
The next big revolution in manufacturing is just beginning. The world is getting smaller through technology and communication, and the internet is key in bringing about this transition. Cell phones, cars, watches, home alarm systems, televisions. Devices across the globe are connected, sending data and status updates, controlling what other devices are doing, and providing information necessary for next-step decision making. The interconnectedness of devices and sensors is making its way into the manufacturing processes. Through the use of internet protocols, devices and sensors across the factory lines can track and control product through various stages of development. This control can lead to automated custom manufacturing and efficient made-to-order assemblies. Custom manufacturing will be easier and more cost-efficient through the Industrial Internet of Things.

On a micro scale, being able to track, trace, and control product through the build process leads to the ability to customize the manufacturing steps. This is where the change from linear to dynamic manufacturing lines can be made. Knowing where the product is, as well as where it has been, can control what happens next in the process. At each step along the way, sensors and devices need to be able to push and pull data from a connected hub or intranet. Devices need to communicate with each other or a central hub to report status and quality, and in order to control where the product is going next. A connected set of devices (Internet of Things) is key in building a custom manufacturing line.

by Tim O'Neel on September 22, 2015
posted in Industry Trends
When most of us scan a barcode in 2015, we don't give it a second thought. We just get upset if it doesn’t decode within a second or two. But without the data capture milestone described below, Microscan might be a very different company today.

The first UPC symbol ever scanned at a retail checkout counter was at the Marsh supermarket in Troy, Ohio at 8:01 a.m. on June 26, 1974. It was a 10-pack of Wrigley's Juicy Fruit chewing gum. The shopper was Clyde Dawson. The cashier was Sharon Buchanan. When Ms. Buchanan passed the barcode over her newly-installed laser scanner’s field of view, producing the tell-tale beep (good read!), she made history. The cash register rang up a quaint 67 cents...
Eldad Ben
by Eldad Ben Shalom on July 6, 2015
posted in Solutions & Applications

This is the second of three articles about OCR vision applications that use CIJ printers. In the first article I discussed the typical print output achieved with CIJ printers at high speed. I showed real images of print examples and showed the high degree of variability among similar characters printed by the same printer head.

Once you understand the nature of printer output on high-speed production lines, and have noticed the high variation between one print to the next print, you can begin to identify possible solutions. But before you design your OCR vision solution, you should become familiar with the common defects that are relevant to the specific line you intend to automate. Factory quality managers, technical staff, printer technicians, and any other factory staff who keep records of production errors can help you to identify the common defects on any given production line. The defect identification stage is the most critical. Your goal is to understand the real needs of the customer. Many times the customer needs professional help in order to know what real problems he or she is facing on the production line. It’s not always immediately easy to pinpoint defects and other problems, but there are reliable ways to do so.

Some helpful questions to ask quality managers or other technical staff during this critical defect identification stage are:

by Alex Pete on May 8, 2015
posted in Product Development

In industrial manufacturing, last thing you want to deal with is unreadable barcodes interfering with production. Decoding failures may require downtime for diagnostics and equipment adjustments, which results in unforeseen costs, lost opportunities to meet business objectives, and a lot of frustration. After all, using barcodes in production is supposed to streamline operations, not complicate them! But the fact is that even after you go through the checklist of standard to-dos to prepare your operations with quality barcodes and ideal barcode-reading conditions, some circumstances may be beyond your control and unpredictable no reads may still result.

But help is available. Instead of focusing on the condition of your barcode or the setup of your operations, you may be able to attack barcode readability issues from the barcode reader itself by using more powerful decoding algorithms. In this blog post, I’ll offer some advice about how you can dedicate less of your limited resources and valuable time to barcode reading and more to getting business done.

Eldad Ben
by Eldad Ben Shalom on April 28, 2015
posted in Solutions & Applications
Untitled Document

Insights about OCR Vision Applications with CIJ Printers (Part 1 of 3)

One Monday morning I find this email in my mailbox sent by a customer:

‘Hi Eldad, how is it going? Can you help me with this application: I have a CIJ printer installed on a high speed production line. Several lines of text are printed on each product. I need a vision system for inline inspection of the printed characters. I try using OCR tool, then compare the data read by the camera with a data string sent from the printer and finally generate a warning output signal when mismatch occurs. I find it hard to train the system with one font library to solve all products. I still have lots of false rejects, where the camera rejects good products. I’m on it for several days and still cannot make it work – can you advise?’

by Jason Dobbs on April 3, 2015
posted in Industry Trends

As technology advances, manufacturing is becoming more and more automated. Robots are becoming the standard in most manufacturing lines that require fast, repetitive, precise placement of components. Many other types of automated equipment are being used for inspection to ensure components are placed in specified locations, check for missing components, and ensure fluid levels are at the exact level. Traceability information is collected by reading barcodes on parts in production so Operations knows exactly where every product in the manufacturing plant is at any given time and where each product has been. In order to achieve this type of automation we embed devices like machine vision cameras to give equipment eyes for visual inspection, and auto ID imagers and laser scanners to allow equipment to trace product through the manufacturing process. When developing your automated equipment it is imperative to choose a machine vision system, auto ID imager, or laser scanner that fits your precise requirements. There are five things to consider when choosing an embed­ded machine vision camera, auto ID imager, or laser scanner for an application: barcode type and orientation, inspection parameters, appli­cation speed, integration space, and data communication needs.


by Robert Andersson on January 2, 2015
posted in Solutions & Applications

We frequently get support questions on how to acquire and store data generated by our machine vision or Auto ID readers in some form of file. Microsoft Excel is a widely used tool by many businesses to manage, process, and share data. In this post I demonstrate a way to integrate our Ethernet devices—such as the Vision HAWK and Vision MINI Xi Smart Cameras, and our QX Hawk and MINI Hawk Auto ID readers—and their output into an Excel sheet.

How do you actually retrieve and archive the data that a barcode scanner or smart camera generates? This is a question that we as Solution/Application Engineers often face. In particular, is there a direct way to get the data straight into an Excel sheet without the need for any temporary flat-file storage? Such solution would circumvent any intermediate data import or other data staging procedure, creating a lean framework for data capture and management.

A data management nightmare indeed, Microsoft Excel is still by far the most widely-adopted Business Intelligence tool across all domains of business life. Now, how do you make a smart camera or barcode scanner write its output directly into an Excel sheet without any flat-file data staging or third party software components?

Excel makes use of the MS Windows event-driven programming language called VBA (Visual Basic for Applications). This gives access to the Windows API and the many functionalities offered in the Windows DLLs. In Excel this facility is referred to as 'writing a macro'.

Data arriving over TCP/IP to a Windows host system is being managed by the Windows Socket or Winsock. This Winsock API makes it possible to read and write data across TCP/IP connections.

As with most programming endeavors, all roads lead to Rome, and I took one of them (which, admittedly, was mostly copied and pasted from people knowing more than I do). The VBA code contains one standard module and a class module, see Figure 1. The standard module sets up the connection by calling the class module with arguments such as IP address and port number using user input. The class module initializes the connection and manages the data capture and data writing into the sheet.

by Jocelyn Chen on August 7, 2014
posted in Industry Trends

Since its invention in 1994, the Data Matrix code has become the industry standard in automated tracking and traceability applications in manufacturing, supply chain operations, and beyond. From the UID directive by the U.S. Department of Defense, mandating that a 2D code be included on all government-furnished military and non-military equipment, to new UDI regulations on pharmaceutical and medical devices to also include the 2D codes, Data Matrix has become a staple in most industries worldwide. The code has seen rapid adoption in areas where small footprint and high readability are vital, such as on automotive parts of various substrates, small or space-restricted PCBs, and highly-regulated pharmaceutical labels and packaging. Its ability to store thousands of characters of data within an extremely compact size, including its generous reading tolerance down to 2.5 mil code size, set Data Matrix apart from other barcode symbologies.

This year marks the 20-year anniversary of the Data Matrix code, since its invention by International Data Matrix (I.D. Matrix), a key innovator in Microscan’s 30-year corporate lineage. With the passing of this milestone, it’s only fitting that we shine a spotlight on the impact this symbology has made in helping manufacturers do more with less in automatic identification. In this blog, we’ll take a look at how the Data Matrix code is being used in major industries and by our own customers.


As consumer electronic devices become smaller and smaller, so must their internal components. But just because a microchip in your smart phone has shrunk to only a fraction of the size of its late-19th-century predecessors doesn’t mean that we can indentify it in any more concise terms...
by Shaina Warner on May 22, 2014
posted in Industry Trends

Prevention has become just as important as production in today’s manufacturing world. As wonderful as it is to see dollars streaming in as a result of products going out the door, profits can easily be affected if quality or compliance issues arise after products have made their way to the customer. More and more, the costs associated with producing less-than-optimal-quality products – from customer fines and industry fees to product recalls – are compelling businesses to invest in systematic preventative measures to ensure that they continue to see healthy returns for their production efforts.

Machine vision inspection plays a major role in ensuring production quality. Using automated tools such as cameras and software to read barcodes, check labels, and inspect products for defects, vision systems provide the equivalent of having several sets of eyes constantly monitoring your operations, making decisions about which products are up to par and which should be rejected. Many businesses rely on machine vision to automate quality control on their production lines with real success, but as good as a vision system may be, it is only one tool in helping companies maintain accountability for their products and activities in production...