Portfolio
Computer Vision for Technical Drawings
Industry:
AEC / CAD Documentation
Built for architecture and engineering teams to identify and classify technical symbols across large drawing sets without manual inspection.
Problem
The manual processing of technical plans is slow due to the complexity and visual noise inherent in engineering drawings.
Overlapping Elements:
Plans contain overlapping lines and text that obscure key symbols.
Visual Noise:
Scanned or converted documents often include artifacts that interfere with detection.
Structural Variability:
Symbols vary in scale and rotation across different document sets.
Analysis Effort:
Reviewing large drawing sets for specific components is labor-intensive.
Solution
The system uses a computer vision pipeline to automate the detection and classification of symbols directly from document layers.
Primitive Extraction:
Uses tools for PDF vector layer exploration and primitive extraction to identify basic geometric shapes.
Synthetic Labeling:
Generated training data to improve model accuracy on rare or specific technical symbols.
Detection Pipeline:
Deployed an architecture for symbol detection and matching that handles overlaps and variability.
Iterative Optimization:
Refined recognition quality through model experimentation and rule-based logic tuning.
Tech Stack
Analysis:
Tools for PDF vector layer exploration and primitive extraction.
Computer Vision:
Object detection and symbol matching models.
Process:
Synthetic data generation and iterative model tuning.
Results
Reduced Manual Effort:
Decreased the time required for symbol identification and classification.
Analysis Speed:
Accelerated the processing of large drawing sets for rapid engineering review.
Precision:
Improved recognition quality through the combination of AI and rule-based logic.
Data Structure:
Transformed unstructured PDF drawings into organized lists of technical components.
Complex web engineering & SaaS