Optical Character Recognition (OCR) has revolutionized the way in which that companies automate document processing. Nonetheless, the standard and accuracy of the technology doesn’t cut it for each application. The more complex the document being processed, the less accurate it becomes. This is particularly true for engineering drawings. Although out of the box OCR technologies will not be suited to this task, there are other ways to realize your document processing goals with OCR. In what follows, I’ll explore several viable solutions to provide you a general idea without going into an excessive amount of technical detail.

Challenges of Engineering Drawing Recognition

In the case of technical drawings, OCR struggles to grasp the meaning of individual text elements. The technology can read the text, but it surely doesn’t understand its meaning. There are a variety of opportunities for engineers and manufacturers to contemplate if the automated recognition of the technical document is configured appropriately. See probably the most significant of them below.

To attain complex technical documentation evaluation, engineers have to train AI models. Similar to humans, AI models need experience and training to grasp these drawings.

One challenge of blueprints and engineering drawings recognition is that the software must understand easy methods to separate the various views of the drawing. These are different parts of the drawing that give a basic idea of its layout. By separating the views and understanding how they relate to 1 one other, the software can calculate the bounding box.

This process may include several challenges:

• Views might overlap
• Views is perhaps damaged
• Labels is perhaps equidistant to 2 views
• Views is perhaps nested

The connection between views is one other possible issue. It’s essential to consider whether the view is a flat a part of the diagram, a turned part, a block, or something else. Moreover, there could also be other problems like chained measures, missing annotations, implicitly defined heights through reference to a normal, or other problems.

Importantly, generic OCR cannot reliably understand text in drawings that’s surrounded by graphical elements like lines, symbols, and annotations. For this reason fact, we’d like to dive deeper into OCR with machine learning which can be more helpful for this application.

Pre-Trained and Custom OCR Models

There’s no shortage of OCR software available on the market, but not all of this software could be trained or modified by the user. As we’ve learned, training could also be a necessity for analyzing your engineering drawings. Nonetheless, OCR tools for these sorts of drawings do exist.

Pre-Trained OCR Tools

Listed below are some common options for OCR recognition of engineering drawings:

• ABBYY FineReader: this versatile blueprint-interpretation software offers OCR technology with recognition capabilities for text. It supports various image formats, layout retention, data export, and integrations.
• Adobe Acrobat Pro: along with providing PDF editing, viewing, and management, Acrobat lets you scan OCR documents and blueprints, extract text, and perform searches. It supports various languages and allows users to configure options.
• Bluebeam Revu: one other popular PDF application, Bluebeam Revu offers OCR technologies for engineering drawing text extraction.
• AutoCAD: standing for Computer Aided Design, AutoCAD supports OCR plugins for interpreting blueprints and converting them into editable CAD elements.
• PlanGrid: this software includes blueprint OCR interpretation out of the box. With this feature, you possibly can upload blueprint images after which extract, organize, index, and search the text.
• Textract: this cloud-based AWS feature enables OCR evaluation of documents and might extract elements like tables from documents. It could possibly also recognize elements from blueprints and provides APIs for integration with other applications.
• Butler OCR: providing developers with document extraction APIs, Butler OCR combines machine learning with human review to reinforce the accuracy of document recognition.

Custom OCR Solutions

In the event you’re in search of custom OCR solutions that could be trained to realize higher automatic data extraction from engineering drawings and adopt it to your specific data format, listed here are a couple of popular options:

• Tesseract: this versatile, open-source OCR engine maintained by Google could be trained on custom data to acknowledge blueprint-specific characters and symbols.
• OpenCV: Open-Source Computer Vision Library could be combined with OCR tools like Tesseract to construct custom interpretative solutions. Its image processing and evaluation functions can enhance the accuracy of OCR on engineering drawings when properly utilized.

Except for these tools, it’s also possible to independently develop custom machine learning models. By utilizing training models on labeled datasets, frameworks like TensorFlow or PyTorch, these solutions could be fine-tuned to acknowledge specific blueprint elements and achieve higher accuracy for the needs of a corporation.

Pretrained models offer convenience and ease of use but will not be as effective at interpreting engineering drawings as custom solutions. These custom solutions also require additional resources and expertise to develop and maintain.

Custom solutions require additional financial resources and labor to develop. I might recommend starting with a proof of concept (PoC) to validate technical capabilities and a minimum viable product (MVP) to envision the market’s perception of the project before investing too heavily in a custom OCR solution.

The Strategy of Implementing an OCR Module for Reading Engineering Drawings

The most effective place to begin constructing OCR software for engineering drawings can be to research available open-source tools. In the event you exhaust your open-source options, it’s possible you’ll have to turn to closed-source options with API integrations.

Constructing an OCR solution from scratch is impractical since it requires an enormous dataset for training. That is difficult and expensive to assemble and requires a number of resources for model training. Usually, fine-tuning existing models should fit your needs.

The method from here looks something like this:

1. Consider requirements: you’ll want to understand what form of engineering drawings your application should work with and what sorts of features and functionalities are needed to realize that goal.
2. Image capture and pre-processing: take into consideration what devices you propose to make use of to capture the photographs. Extra pre-processing steps could also be needed to reinforce the standard of your results. This will likely include cropping, resizing, denoising, and more.
3. OCR integration: consider the OCR engine that may work best along with your application. OCR libraries have APIs that allow your application to extract text from captured images. It’s essential to contemplate open-source OCR solutions for cost-savings. Third-party APIs could be fickle with regard to pricing over time or lose support.
4. Text recognition and processing: next, it’s time to implement logic to process and recognize text. Some possible tasks it’s possible you’ll consider adding on this step are text cleanup, language recognition, or another techniques that may provide clearer text recognition results.
5. User interface and experience: an easy-to-use UI for the app is significant in order that the user can effectively use it to capture images and initiate OCR. The outcomes must be presented to the user in a way that’s easy to grasp.
6. Testing: thoroughly test the applying to make sure its accuracy and usefulness. User feedback is important to this process.

Wrapping Up

In face of the challenges of making OCR software for complex engineering drawings, organizations have a variety of options available to them to approach the problem. From a spread of pre-trained models and customizable tools to create more personalized solutions, businesses can find ways to effectively analyze, index, and search through blueprints and other complex documents. All it takes is a few ingenuity, creativity, and time to craft an answer that meets their needs.