Guide for Developing OCR Systems for Blueprints and Engineering Drawings
In this article, we’ll explain how the OCR technology works for reading blueprints and engineering drawings, and what OCR models can be used in various use cases. We will also overview free and paid tools available for integration and take a look at the steps to build a custom system for various business purposes.
6 Reasons to Use OCR for Blueprints Reading
Automating blueprint reading with OCR (Optical Character Recognition) entails the use of computer vision techniques and algorithms to extract relevant information from engineering blueprints or technical drawings. In the context of blueprint reading, OCR is utilized to analyze and interpret the textual content present in the blueprints, such as annotations, labels, dimensions, notes, and other relevant details.
The OCR technology can be tailored for engineering drawings and blueprints due to several reasons:
- Text extraction: OCR is designed to extract text from images, making it ideal for capturing textual information present in blueprints, such as labels, titles, dimensions, and annotations. It can also accurately identify and convert alphanumeric characters into machine-readable text.
- Structured layout: blueprints and engineering drawings often have a structured layout with labeled sections, grids, and tables. OCR algorithms can analyze the layout and identify the structure of the document, preserving the organization and hierarchy of the information. This helps in maintaining the original blueprint’s structure and facilitates easy interpretation.
- Technical dataset implementation: engineering drawings typically contain specialized technical characters, symbols, and notations specific to the field. OCR systems used for blueprint reading are often trained on and designed to handle these technical character sets, allowing them to accurately recognize and interpret the unique symbols and notations used in engineering drawings.
- Efficiency and accuracy: OCR technology has undergone significant advancements over the years, resulting in high accuracy rates for text recognition. This helps minimize manual data entry and transcription errors, improving overall efficiency and productivity.
- Searchability: by converting paper-based blueprints into machine-readable text, OCR enables the digitization and indexing of engineering drawings. This makes it easier to store, search, retrieve, and share blueprint information digitally. It also enhances accessibility and enables efficient management of large blueprint archives.
- Integration with CAD and BIM: OCR can be integrated with Computer-Aided Design (CAD) and Building Information Modeling (BIM) software systems commonly used in engineering and construction industries. This integration allows for automatic data transfer from blueprints to CAD/BIM models, streamlining the design and construction processes.
OCR for Blueprints and Engineering Drawings Reading in a Nutshell
Overall, OCR is a technology that allows converting text from images, photos, or handwritten documents into machine-readable format. Here is the role it can play when it comes to the reading of blueprints and engineering drawings.
Here’s a brief explanation of the steps required for blueprints and engineering drawings recognition with OCR.
The engineering drawing or blueprint is first scanned or photographed using a digital device such as a scanner or a camera. High-resolution images are preferred to capture the fine details of the blueprint.
2. Image pre-processing
Before OCR can be applied, the scanned image may need some pre-processing to enhance its quality and readability. This step can involve tasks like noise reduction, image rotation, cropping, and contrast adjustment to optimize the image for OCR.
OCR software analyzes the processed image to identify and extract the text presented in the engineering drawing. It uses advanced algorithms to recognize individual characters and forms based on their shapes, patterns, and other features.
4. Character recognition
OCR algorithms attempt to match the extracted text against a database of known characters. This process involves pattern recognition and statistical analysis to determine the most likely match for each character.
5. Structure recognition
For improving the results, OCR can also analyze the layout and structure of the blueprint to identify headers, titles, labels, and other elements. This helps in organizing the extracted text and preserving the original structure of the source.
The OCR software generates machine-readable text output that can be used for various purposes such as indexing, searching, archiving, or further analysis.
Depending on the quality and accuracy of the OCR output, some post-processing steps may be required. This can involve manual proofreading, spell-checking, and error correction to refine the extracted text and ensure accuracy.
As engineering drawings and blueprints involve specific elements and symbols, there might be multiple AI models, which in combination with OCR, can help to achieve higher accuracy and better results for specific tasks:
- Image segmentation: this involves dividing, extracting, and suppressing the information into categories: information block and tables, geometric dimensioning and tolerancing (GD&T) information.
- Information processing: text detection and recognition in the information block.
- GD&T processing: dual recognition model to detect special symbols and regular characters.
- Dimension processing: Prediction and recognition on patches of the processed image, with post-processing techniques on recognition and a tolerance checker algorithm.
Or we can train a custom model. If the client already has a labeled dataset, the quality of the output will be even better, as the model uses unique data from the client for the training.
In general, the model of blueprint interpretation might look like this:
Features and Challenges of Analyzing Blueprints with OCR
OCR-based blueprint analysis offers significant advantages in terms of time savings, data interpretation accuracy, and improved accessibility to information. However, it may also encounter challenges related to complex layouts, technical symbols, handwriting recognition, and image quality. Proper selection of OCR software, image preprocessing, and verification tools are crucial for ensuring the reliability of the extracted data.
Features of Analyzing Blueprints with OCR
Developing OCR systems for blueprints and engineering drawings involves several aspects of data handling:
1.Data gathering and labeling
Training an AI model for blueprint analysis usually requires a large dataset of blueprint images. These images can be gathered from various places, such as repositories, in-house archives, or cooperations with organizations that might provide sufficient blueprint data.
To train a model, the blueprint images need to be labeled with corresponding annotations or tags. This labeling process involves human experts or annotators who mark specific elements in the blueprint, such as dimensions, labels, symbols, and other relevant information.
2. Data pre-processing
This step can be crucial as engineering writings typically require noise reduction, image rotation, cropping, resizing, and contrast adjustment. These steps can improve the model’s ability to recognize and interpret the blueprint elements.
3. Data storing and transformation
To store and manage the extracted data, a suitable database configuration is implemented. The database structure may vary depending on the specific requirements and use cases for various domains. It can involve creating tables, fields, and relationships to organize the extracted blueprint information effectively.
In many cases, the extracted data may require further transformation to align with specific formats or standards. For example, converting the extracted measurements to a specific unit system, transforming data into standardized codes, or reformatting the extracted text to adhere to specific guidelines.
4. Model training
Training a model for blueprint analysis often involves an iterative process. Initially, the model is trained on a subset of the labeled data, and its performance is evaluated. The model’s predictions are compared with the ground truth labels to assess its accuracy and identify areas for improvement. Based on the evaluation, the model may undergo further training with adjusted parameters or architectures to enhance its performance. This process continues until the desired level of accuracy and reliability is achieved.
It’s important to note that the specifics of data extraction, preprocessing, and model training can vary based on the specific goals and requirements of the project. The expertise and domain knowledge of the team involved plays a crucial role in developing effective training processes and configurations.
Blueprint and Engineering Drawings Reading Challenges
First of all, analyzing complex technical documentation is a challenge itself. It requires well-trained engineers with wide experience and high qualifications. Just like humans, AI models need specialized training to understand these drawings. Let’s take a look at some of these challenges and understand how they can turn out to be more complex than it may seem.
Engineering drawings and blueprints include various views that give a basic idea about the layout of the drawing. To calculate the bounding box, it’s necessary to separate the views and then understand how the views relate to one another.
The separation can be simple, but in many cases, there are additional challenges to consider:
- views might overlap
- views might be damaged
- labels might be equidistant to two views
- views might be nested, and so on
Another problem might be connected with the relationships of views as it is important to understand how the views relate to each other. We must determine whether we are dealing with a flat part of a diagram, a turned part, a block, etc.
Plus, there might be many other problems to consider: chained measures, missing annotations, implicitly defined heights through reference to a standard, etc.
Obviously, not all OCR solutions can handle the complexity of technical drawings, so let’s dive deeper into the solutions that can be helpful for blueprint analysis.
Available Software for Blueprint Interpretation with OCR
You can find a vast array of OCR software on the market, but it’s important to note that not all OCR products provide users with the ability to “train” the recognition on their own. Technical drawings include multiple prints and additional variables that might be a problem for common OCR solutions.
Pretrained OCR Tools for Blueprints Reading
There are several software tools available for blueprint interpretation that can offer OCR capabilities. Here are some common options:
- ABBYY FineReader is a versatile OCR software that can be used for blueprint interpretation. It offers advanced OCR technology with intelligent recognition capabilities for text extraction from blueprints. It supports various image formats, provides layout retention, and offers options for data export and integration with other applications.
- Adobe Acrobat Pro is a comprehensive PDF editing and document management software that includes OCR functionality. It allows you to scan and OCR blueprints, extract text and perform searches within the document. The OCR feature in Adobe Acrobat Pro can handle different languages and provides options for optimizing OCR accuracy.
- Bluebeam Revu is a popular software for PDF creation, markup, and collaboration. It includes OCR capabilities that enable text extraction from blueprints and other construction documents. The extracted text can be searched, selected, and copied for further use.
- AutoCAD, a leading software for computer-aided design (CAD), can be extended with OCR plugins to enable blueprint interpretation. These plugins integrate OCR technology into AutoCAD, allowing for text extraction from blueprints and converting it into editable CAD elements.
- PlanGrid is a construction productivity software that includes OCR capabilities for blueprint interpretation. It allows you to upload blueprint images, extract text using OCR, and organize the extracted information. The OCR feature in PlanGrid helps with searching, indexing, and navigating through blueprints.
- Textract by AWS is a cloud-based OCR service provided by AWS. It offers pre-trained models for document analysis. Textract can extract text, tables, and other elements from blueprints and provides APIs for integration with custom applications.
- Butler OCR offers document extraction APIs for developers. It combines machine learning with human review to ensure extraction accuracy on any document
These software tools provide OCR functionality, allowing extracting text and other relevant information from blueprints and engineering drawings. It’s important to evaluate the features, compatibility, and pricing options of each software tool to determine which one best fits the specific needs and requirements.
Custom OCR Solutions for Blueprint and Engineering Drawings Reading
Custom OCR solutions surpassed generic OCR tools in various aspects and can help to achieve better automatic data extraction from blueprints. Among popular custom OCR solutions are:
- Tesseract is an open-source OCR engine maintained by Google. While it doesn’t offer pre-trained models specifically for blueprints, it can be trained on custom data to recognize blueprint-specific characters and symbols. Tesseract provides flexibility for building custom OCR solutions tailored to blueprint interpretation.
- OpenCV (Open Source Computer Vision Library) is a popular open-source computer vision library that can be combined with OCR tools like Tesseract to build custom OCR solutions for blueprint interpretation. OpenCV provides image processing and analysis functions that can enhance the accuracy of OCR on blueprints.
To meet unique business needs in various industries, organizations can develop completely custom Machine Learning models. This involves training models on labeled blueprint datasets, using frameworks like TensorFlow or PyTorch. Custom models can be fine-tuned to recognize specific blueprint elements and achieve higher accuracy for the needs of the organization.
It’s important to note that both pre-trained OCR software and custom solutions have their pros and cons. Pretrained models offer convenience and ease of use but may not be as accurate or specialized for blueprint interpretation. Custom solutions provide flexibility and the potential for higher accuracy but require additional resources and expertise to develop and maintain. When it comes to custom models, they have a big potential for distribution on the market, as they offer unique functionality and better results. However, they require additional financial and labor resources to develop. In this case, starting with a PoC to validate technical capabilities and an MVP to check the market’s perception of the product might be a smart idea.
Integration of OCR modules
Integrating OCR modules with manufacturing software can bring significant benefits in terms of automation, data extraction, and efficiency. The specific software used for integration depends on the manufacturing software in use and the requirements of the project. Here are some examples:
1.Enterprise Resource Planning (ERP) Systems
SAP ERP: SAP provides various modules and integration options for OCR, including integration with their Intelligent Robotic Process Automation (RPA) solution. OCR can be integrated with SAP ERP to automate data entry from blueprints, invoices, purchase orders, and other manufacturing documents.
Oracle ERP: Oracle offers integration capabilities with OCR modules through its Oracle Intelligent Document Recognition (IDR) solution. This enables the extraction of data from blueprints and other manufacturing-related documents directly into Oracle ERP systems.
2. Product Lifecycle Management (PLM) Systems
Siemens Teamcenter: OCR modules can be integrated with Siemens Teamcenter PLM software to automatically extract and populate data from blueprints into the PLM system. This streamlines the design and manufacturing process by reducing manual data entry.
PTC Windchill: PTC Windchill PLM software can be enhanced with OCR integration to extract relevant information from blueprints and update the PLM database automatically. This ensures accurate and up-to-date information in the product development lifecycle.
3. Computer-Aided Design (CAD) Software:
Autodesk AutoCAD: AutoCAD can be integrated with OCR modules to extract text and dimensions from blueprints and automatically update the CAD drawings. This streamlines the design process and reduces manual data entry efforts.
Dassault Systèmes CATIA: OCR integration with CATIA enables the extraction of text, annotations, and other information from blueprints and populates the CAD model with relevant data. This enhances collaboration and data synchronization between blueprints and CAD designs.
4. Manufacturing Execution Systems (MES)
Siemens Opcenter (formerly Camstar): OCR modules can be integrated with Siemens Opcenter MES to extract data from blueprints and automate work instructions, quality checks, and production reporting. This ensures accurate data capture and improves productivity on the shop floor.
5. Custom solutions
Custom-built manufacturing software can also incorporate OCR modules using OCR libraries or APIs such as Tesseract, ABBYY, or Google Cloud Vision. This allows to create tailored OCR integration to meet specific manufacturing requirements, including blueprint interpretation and data extraction.
When integrating OCR modules with manufacturing software, it’s essential to assess the specific needs of your project, evaluate the compatibility and integration options provided by the manufacturing software, and select the OCR module or custom solution that aligns best with technical and business requirements.
MobiDev’s Approach to Building Apps to Read Blueprints and Engineering Drawings with OCR
Imagine you need to build an application with OCR that will read blueprints and engineering drawings to digitize the working process and extract information in a usable form. Where should you start? Our experienced AI team will help you to define the necessary steps of the process depending on your domain and business needs. Actually, OCR technology can be utilized in almost all industries — anywhere where there is a need to recognize text from an image. Machine-printed texts can usually be recognized with better quality results than hand-written texts, but of course, everything depends on which goals you need to achieve.
We’ll analyze available open-source tools with the combination of pre- and post-processing techniques which can be used to meet the requirements. If nothing from the open-source list is able to solve the tasks, we’ll analyze available paid solutions on the market which have APIs and are easier to integrate with your product, meeting the expected quality of generated output. Building an OCR solution from scratch is mostly impractical as it requires a huge dataset that is difficult to gather and a lot of resources for training the model itself. In most cases, fine-tuning the existing model might be a good solution for unique business needs.
Here is a brief overview of the process:
- Requirements. Think of the specific features and functionalities your app should have. What kind of blueprints and engineering drawings do you work with? What specific information does your app need to recognize and extract? Consider such aspects as supported languages and specific symbols, image capture options, text recognition accuracy, and the possibility of integration with other services.
- Image capture and preprocessing. Another important aspect is the functionality to capture images. What devices are you going to use? What is the expected image quality that will be passed to the OCR engine? For enhancing the quality of results, our AI team would likely need to add extra preprocessing steps such as image cropping, resizing, image denoising, etc. for being able to obtain better text recognition output.
- OCR integration. We consider what kind of an OCR engine or library is better to integrate into your app. Popular OCR libraries include Tesseract, Google Cloud Vision API, Microsoft Azure OCR, and ABBYY Mobile OCR SDK. These libraries have APIs to perform OCR on the captured images and extract text from them. We usually are considering open-source OCR solutions in terms of provided quality and if results are satisfied — we would proceed with this approach in order to save budget and not be dependent on third-party APIs.
- Text recognition and processing. Next, we need to implement logic to process the recognized text. This may include tasks like text cleanup, language detection, or any other NLP techniques that could help to provide clearer text recognition results.
- User interface and experience. It’s also necessary to design and develop an intuitive user interface (UI) for the app. It’s important to create a user-friendly experience for capturing images, initiating OCR, and presenting the recognized text to the user.
- Testing. Testing is crucial to ensure proper functionality and accuracy. With user feedback, it’s easier to refine the app.
Automated blueprint and engineering drawing interpretation comes with additional value to the business, and blueprint searchability is the beginning. Once the data capture process is finished, the information can be easily processed and analyzed. Artificial intelligence-related technologies can be utilized for sophisticated data analysis and mining data-centric insights for business decisions and operations.
Let’s Start Building Your System Together!
At MobiDev, we believe in helping our clients solve industrial challenges with the best of AI and computer vision technology. The MobiDev AI team can help to create a custom OCR solution tailored to your specific needs and integrate it into your system. We understand the complexity of each project that involves OCR and we are ready to dive deep into the domain to create a tailored, out-of-the-box solution to meet your unique business needs. Get in touch, explain your product vision, and we will help you choose the right technological solution for it.