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Engineering drawings represent a unique challenge for data capture staff. They are complex and packed with essential information that needs to be recorded– some in predictable locations, some not. They become even more challenging to deal with when you factor in handwritten notes that often appear on engineering drawings, forcing firms to use manual data entry practices that cost a substantial amount of time and money.
In this guide, we will discuss how technology like optical character recognition, or OCR for engineering drawings, can modernize and automate the data entry process, improving accuracy and reducing cost. As a result, engineering firms and other businesses that audit these drawings can focus their budgets on more high-skill work to support the innovation they provide.
The Problem: Manual Data Entry
As mentioned above, manual data entry for engineering drawings is expensive. This isn’t just a reference to financial costs, but also labor hours. At many engineering firms, entry-level data analysts aren’t the only ones spending time on data entry, with high-skill engineers and designers having to spend their time handling data, since their expertise is sometimes required to read complex drawings or train data entry staff to do so.
The drawbacks don’t stop there:
Drawbacks of Manual Data Entry
| High Labor Costs | A high financial and time investment is required to analyze engineering drawings and enter their complex data. |
| Slow Data Processing | Manual data entry takes a long time, from scanning a drawing to the input and organization of all the data. |
| Inconsistent Data Accuracy | Manual data entry processes are fraught with human error, which can lead to costly mistakes regarding part sizes, quantities, and other issues that waste time, money, and materials. |
| Difficult-to-Read Data | Most manual data entry processes use outdated tools like Excel to organize data manually, making it difficult to track down and read the data that users need. |
| Complex Data Organization and Exporting | These outdated tools also make it difficult to centralize data organization or export it to tools that make centralization easier. |
These issues make manual data entry a liability to the firms that use it. Historically, the complexity and diversity of engineering drawings made it nearly impossible for data entry to be automated, since simple text-analysis tools couldn’t pull alphanumerical characters from images wherein the data isn’t placed in the same location every time.
Recently, though, innovative software technology has offered a solution.
The Potential Solution: Optical Character Recognition (OCR)
Optical character recognition (OCR) technology allows software tools to recognize alphanumeric characters in images. OCR has been around for decades, but it needed serious evolution before being useful for common commercial data entry applications.
Once OCR tools could read characters regardless of their typed or printed font, they became helpful in software platforms that paired them with automated data entry technology. These OCR data entry tools could pull alphanumerical data from a given section of an image and input it into a corresponding section of whatever data organization tool (Excel, QuickBooks, etc.) with which they were integrated.
Firms that deal with engineering drawings were still left without a solution, though, as there was no way to leverage OCR in dynamic images like engineering drawings, where much of the data wasn’t reliably placed in the same location every time. Additionally, information left in static sections, like parts lists or keys, couldn’t be identified intelligently to allow the software to automatically recognize what measurement, part, or material the values refer to. This made it impossible for the tool to ensure all data was inputted in the proper cell and format.
This is where the marriage between OCR and another key, cutting-edge technology became the solution for engineering firms, municipalities, and other firms that frequently analyze engineering drawings.
The Evolution: Machine Learning OCR with Automated Data Entry
AI and machine learning were the key to adapting OCR technology to use cases as complicated as data entry from engineering drawings. With machine learning OCR, software can be taught to locate information – even when it is found in different areas of each image– by feeding AI learning models other examples of engineering drawings to provide “context.” AI tools use this context to recognize how data is usually organized and automatically enter this data into databases with which they are integrated. Machine learning allows these tools to improve reliability and accuracy over time as they analyze more images.
Pairing OCR with AI and machine learning also helps on the entry side, allowing tools to input data wherever needed– even if that involves entering it into multiple databases. Commercial tools generally try to centralize all data in one database. Still, AI makes it easy to input and export across multiple platforms for businesses that are hesitant to overhaul their existing software stack.
This automation functionality provides a wide variety of benefits:
The Benefits of Machine Learning-Paired OCR
| Reduced Labor Cost | AI and machine learning automate data entry from engineering drawings, reducing the labor hours spent on data capture and saving money and valuable time for high-skill employees. |
| Improved Data Accuracy | Machine learning OCR tools improve the reliability and consistency of data entry by reducing the risk of human error. |
| Faster Data Capture and Organization | Machine learning data entry tools can capture and organize data far faster than human analysts, giving high-skill employees access to necessary information earlier. |
| Simplified Exporting | AI technology assists with database integration, simplifying exporting data to existing software and allowing automated OCR data entry to fit seamlessly into existing business operations. |
| More Searchable & Readable Data | Automated data entry tools provide clean, readable dashboards with convenient search filters to help users find and analyze specific data from a large volume of stored data. |
To maximize the value of machine learning OCR tools, businesses need to find the right partner to create custom tools informed by as much context as possible.
How to Implement Machine Learning OCR for Engineering Drawings
The best way to implement complex AI tools is to work with a firm that can create purpose-built tools from the ground up for your business. Finding a partner who will spend the proper time consulting with your leadership, performing a deep dive into your operations, and informing their AI tools with a wide library of the most relevant context is the key to unlocking the benefits outlined above.
Accessible support and onboarding assistance are also vital features, as ease of transition allows firms to experience these benefits sooner. Partners that provide open channels of communication from day one onward will provide far more value than those that force firms to reach out during the provider’s business hours.
Finally, while the best possible way to implement any automated data entry tool is to allow your developer to integrate a centralized database as part of their AI platform, firms that are capable of integrating their tools with existing software stacks may be the only option for firms that aren’t interested in a total data capture overhaul.
iTech offers machine learning OCR, and automated data entry, with all of these features and more.
Adopt Machine Learning OCR Tools with iTech
Not all tools are created equal in terms of data accuracy, integration capabilities, and ease of use. Just as software platforms aren’t created equal, their developers and support staff aren’t either. We at iTech pride ourselves on our top-of-the-line onboarding experience and ongoing support, with 24/7 access to support personnel and senior account managers. To learn more about OCR for engineering drawings, or get a consultation, fill out the contact form below.
Reach out to our team today!
IDS Commander iTech2021
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