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Silwatech is backed by a team of 1000+ employees, serving clients in over 10+ industries across 9+ locations
AI-Powered Electrical Symbol Detection and Automated Takeoff for Construction Drawings
The end client is an engineering organisation operating within the construction sector. Its teams prepare electrical quantity takeoffs and cost estimates from technical construction drawings.
The organisation required a reliable method for identifying and counting electrical symbols across dense drawing sets without the time demands and error risks associated with manual tallying.
The end client’s identity is withheld due to contractual confidentiality and NDA obligations.
Project Overview
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- Project Overview
The project involved the development of an AI-powered electrical-symbol detection and automated takeoff system.
The solution was designed and developed by a SilwaTech partner company through SilwaTech’s partner delivery ecosystem.
The system covered
- Detection of electrical symbols within construction drawings
- Automated interpretation of drawing legends
- Classification and counting by symbol type
- Mapping of detected symbols to drawing locations
- Engineer review and correction
- Export of approved quantities to takeoff and estimation software
The solution was designed to automate repetitive counting while preserving engineer authority over final takeoff figures and cost-estimation inputs.
Case Study Topics
Business Challenge
- Electrical takeoff preparation involved several time-consuming and error-prone activities.
Key challenges included
- Manual counting of electrical symbols on every drawing
- More than eight hours of manual effort per electrical sheet
- Dependence on individual sheet legends and drafting conventions
- Cost estimates being prepared from manually recorded quantities
- Dense drawings containing overlapping lines, annotations and symbols
- Variations in drawing scale, quality and layout
- Different symbol styles across projects and drafting teams
- Limited traceability between final quantities and source locations
The client needed an AI-assisted system capable of detecting, classifying and counting symbols while allowing qualified engineers to review and approve the final quantities.
Solution Architecture
Solution Delivered
Computer Vision Symbol Detection
A deep-learning detection model was developed to identify electrical symbols across complex construction drawings.
Key capabilities included
- Detection of electrical symbols on dense drawings
- Recognition of symbols surrounded by lines and annotations
- Model training using representative client drawings
- Support for varying drawing scales
- Handling of different drawing densities and drafting styles
- Confidence scoring for detected symbols
- Visual highlighting of detected items
Legend Interpretation and Classification
OCR and classification capabilities were introduced to connect detected symbols with the corresponding drawing legend.
The solution provided
- OCR-based extraction of legend text
- Identification of symbols included in each legend
- Mapping of detected symbols to defined types
- Classification by symbol category
- Counting by type, quantity and drawing location
- Structured linking of quantities to source symbols
- Exception flagging for uncertain classifications
Engineer Review and Correction
A human-in-the-loop review interface allowed qualified engineers to validate AI-generated results.
Capabilities included
- Visual review of detected symbols
- Confirmation of correctly classified items
- Correction of missed or incorrectly detected symbols
- Reclassification of uncertain results
- Adjustment of final quantities
- Engineer approval before takeoff completion
- Traceable records of reviews and corrections
Takeoff and Estimation Integration
Approved quantities were converted into structured outputs for existing estimating workflows.
The solution supported
- Export of confirmed symbol counts
- Quantities structured by electrical-item type
- Direct handoff to takeoff and estimation software
- Reduced manual re-entry of quantities
- Traceable connection between estimates and source drawings
- Integration with established engineering workflows
Technology Stack
The technology environment included
- Computer vision for electrical-symbol detection
- Deep-learning object-detection models
- Training datasets derived from representative electrical drawings
- OCR for sheet-legend interpretation
- Classification and per-type counting logic
- Confidence scoring and exception handling
- Engineer review and correction tools
- Integration with electrical takeoff and estimation software
- Support for deployment within controlled client environments
Engagement Model
The project was delivered through SilwaTech’s partner ecosystem under a partner-led implementation model.
The solution architecture, AI model development, OCR integration, review-tool development, testing and technical implementation were performed by an experienced SilwaTech partner company. SilwaTech contributed through engagement alignment, partner collaboration and coordination of the required computer vision and construction-technology capabilities.
The cross-functional delivery team included specialists in
- Computer vision research
- Deep-learning model development
- OCR and document processing
- Construction drawing analysis
- Product and interface development
- Estimation-system integration
- Quality assurance and model testing
Delivery progressed through four stages
- Analysis of drawing types, legends, and counting requirements
- Training of the detection model using representative electrical drawings
- Tuning of legend interpretation, classification and counting logic
- Implementation of the engineer-review interface and estimation export
The identities of the end client and implementation partner remain confidential. SilwaTech does not represent itself as the direct contracting party or sole technical implementer for this engagement.
Security and Governance Focus
Construction drawings and cost-estimation information represented commercially sensitive project data.
Key areas of focus included
- Processing within the client’s controlled environment where required
- Encryption of drawings and generated outputs in transit and at rest
- Role-based access to drawings and takeoff results
- Traceability between each quantity and its source symbol
- Confidence scoring and review of uncertain detections
- Engineer confirmation before quantities were finalised
- Controlled export to estimation systems
- Support for on-premises or isolated deployment where required by policy
These controls protected sensitive engineering information and retained accountability for final estimates with qualified professionals.
Value Delivered
The reported outcomes included
- Reduction in takeoff time from more than eight hours to under 20 minutes per sheet
- Symbol-counting performance described as near-human during project evaluation
- Consistent classification and counting across complex drawings
- Traceable quantities linked to source symbols
- Reduced dependence on manual tallying
- Engineers redirected from repetitive counting to review and verification
- Structured quantities prepared for estimation
- Reduced manual transfer of information between workflows
- Direct export into existing takeoff and estimation tools
The reported performance figures should be presented as project results and not as universal accuracy or processing guarantees across every drawing type.
Why Silwatech
This engagement demonstrates the type of applied computer vision capability SilwaTech can support through its technology partner ecosystem, including
Access to specialists in technical-drawing analysis
Experience with dense and visually complex engineering documents
Deep-learning and OCR integration
Human-in-the-loop workflows that retain engineer control
Integration of AI-generated outputs with existing estimation processes
Flexible deployment within controlled environments
Structured collaboration with specialised implementation partners
Selected Enterprise & Public Sector Experience
Our project experience spans government authorities, regulated enterprises, and major organisations across multiple industries.
References shown may include direct SilwaTech engagements and work delivered through our contracted partner network. Inclusion does not imply a direct contractual relationship with, or endorsement of, SilwaTech.
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