Why Can’t OCR Read Your Engineering Diagrams?

At Infrrd, we work with clients across various industries, and despite the differences in their use cases, their initial concerns are often the same. After reviewing numerous case studies, we've noticed a clear pattern. Companies look for tools to automate data extraction from engineering drawings, and invest in OCR solutions, only to be frustrated when important details are missed. This often forces them back to manual extraction, but at what cost? They end up with even more delays, errors, and increased costs for human labor.

Reports suggest that over half of the total cost (50-60%) of implementing OCR technology is spent on correcting its mistakes. That means that for every $1,000 you invest in an OCR system, $500-$600 goes directly toward fixing errors, driving up costs significantly.

But what if there’s a better way to extract data from your engineering drawings without the delays, manual corrections, and overruns? There's a smarter solution out there that can lift the burden of manual extraction once and for all. And no, this isn’t an anti-OCR blog. Instead, it’s about a modern OCR solution that can get your work done the way you always wanted it to.

Why OCR Didn’t Work for You

Whether you're in construction or manufacturing, handling engineering diagrams requires a very different approach than working with standard documents. Traditional OCR is built to recognize character patterns and value patterns within a predetermined template. As a result, it struggles to accurately process images, numbers, diagrams, logos, barcodes, tables, and handwritten text—especially within the fluctuating formats of engineering drawings. Even if you attempt to use it, the error rates can be alarmingly high.

While it’s technically possible to train OCR systems to adapt to new formats and patterns for different document types, the sheer variety and unpredictability of documents circulated in the industry means that the manual effort required to train the system often outweighs the benefits.

Here are some things that OCR just can't handle when it comes to pulling data from engineering drawings.

• Vision Challenges - OCR faces difficulty recognizing elements, numbers, and characters that resemble each other. For example, OCR might read 6 as 8 or 1 as 7, especially with handwritten text. Vision challenges also lead to errors in distinguishing lines and text. For example, often converts texts into exploded vector lines. Similarly, OCR often misinterprets extra characters at the borders as noise, leading to incorrect extractions. Not to mention, many times, similar objects or symbols are detected as the same ones. The amount of manual input needed to correct these can get overwhelming. 

• Scale Inconsistencies Across Drawings - Most engineering drawings have inconsistent scaling systems which OCR doesn’t comprehend. For instance architectural plan showing a floor layout may use a scale of 1:100 for the overall layout, but the details for specific rooms or features (like cabinetry or fixtures) use a different scale (e.g., 1:50). This inconsistency can lead to confusion about the actual dimensions during construction. Similarly, A diagram specifies dimensions in millimeters (mm) for structural elements but uses inches (in) for pipe sizes. Unfortunately, OCR messes up with these details demanding more manual corrections at each step.

• Inaccurate Master Data - OCR inaccurately cross-verifies the master data. There can be multiple reasons behind this like - poor image quality, complex document formatting, conversion errors, non-standard fonts, handwritten text, etc.

• Missing Tolerance Values - The tolerance values in the engineering drawings are often present as super scrips, under scripts, or with upper and lower limits. OCR can’t extract such minute details, often skipping them in most of the extractions. Being a crucial infor in quality control this result is worse than manual extraction. 

• Incorrect Metadata Extraction- OCR makes mistakes in collecting the stamp or metadata especially when stamps are borderless. n documents with complex layouts, a borderless stamp may blend into other text or graphics, further complicating the recognition process.

• Inability to Recognise Rotated Text - OCR is designed to recognize horizontal orientation hence it can’t read rotated or vertical texts. As you know engineering diagrams rotated texts are a common occurrence. Hence someone has to manually enter the rotated texts causing you more delays. 

While it’s technically possible to train traditional OCR systems to adapt to new formats and patterns for different document types, the sheer variety and unpredictability of documents circulated in the industry means that the manual effort required to train the system often outweighs the benefits. If you look at what actually causes these troubles, it all boils down to one thing: OCR reads characters but lacks context. This lack of contextual understanding is what drives people back to manual data extraction, especially when it comes to engineering drawings. Humans naturally interpret nuances and details, understanding how each element or symbol relates to the rest of the components in a diagram. Can a machine really do that?

Imagine you’ve got a team of 10 engineers manually combing through complex engineering drawings, pulling out data, and entering it into different systems depending on what the work demands. It’s time-consuming, repetitive, and let’s face it, not the best use of your team’s skills. Now, picture this—what if that same team could do the work of 30 engineers without breaking a sweat? Suddenly, what used to take hours or even days gets done in a fraction of the time, allowing your team members to focus on higher-value tasks. That’s the kind of productivity boost we’re talking about. Yes, it’s possible - you just need to give your OCR a brain of it’s own.

An OCR That Thinks Like You 

Unlike traditional OCR solutions, modern systems combine the OCR feature with Artificial Intelligence to add a contextual perspective to the process rather than mere character recognition. These are often built on advanced LLMs and can accurately extract up to 80% of the required data from engineering drawings. This is called intelligent Document Processing (IDP). 

Optical Character Recognition (OCR) is a technology that converts different types of documents—such as scanned paper documents, PDFs, or images—into editable and searchable data by recognizing and extracting text.

Intelligent Document Processing (IDP) takes data extraction a step further by integrating OCR with artificial intelligence (AI) and natural language processing (NLP). This allows IDP systems to not only read and extract text but also to understand the context, classify documents, and extract relevant data based on specific business rules. AI mimics human intelligence, so IDP is nothing but AI-enabled-OCR or simply OCR with a human brain.

So IDP can also do a lot of other things that OCR can’t. For example, they can easily integrate with any of the existing tools that you are already using. I’m sure there’s at least a basic CAD and ERP software your team uses. IDP can quickly sync with them and improve the whole process. This helps you avoid a lot of middle management that honestly nobody wants to do. The data extracted from the CAD can automatically be fed into your ERP software. You get the same or better results 3 times faster! No compromise on that.

Same Small Team, Big Money

IDP won’t replace your team, but it’ll definitely take the stress off your plate. You’ll get more done with the same crew, but faster and better. Plus, no need to stress about hiring more people or dealing with rising costs. Here’s a quick breakdown of how Infrrd offers a smarter faster solution that guarantees the best returns for your investments. 

• High Accuracy on the First Attempt
• Template-Free Data Extraction
• SLA-Enabled Document Processing
• Automated RFQ Submissions
• Smooth Integration with Your Existing Software
• Access Actionable Data in Minutes
• Auto-Detect Dimensions with Precision
• Boost Processing Speeds by 400%

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