Automating Filtered Dimension Extraction for Lift Manufacturing
Extracting dimensions with abbreviations filtering and confidence-scored outputs from engineering drawings at scale.
Objectives and Metrics for Success
The client manufactures lifts and elevators, processing 50 to 200 layout drawings daily from both internal AutoCAD systems and external client submissions. Their goal was to automate dimension extraction filtered by abbreviation suffix. Success required four outcomes: extract dimensions matching target abbreviations (WW for shaft width, WD for depth, SS for size, HT for height) from both digital and scanned PDFs, correlate dimensions with associated tabular data, deliver structured Excel output ready for ERP integration, and provide confidence scores enabling risk-based quality control.
The Problem
Processing 50 to 200 layout drawings daily required engineers to manually identify dimensions by abbreviation suffix, interpret varying conventions across document formats, and reformat data for downstream systems. The workflow could not scale without proportional headcount increases.
Mixed Document Formats
Engineering teams received both digital AutoCAD-generated PDFs and scanned client PDFs. Each format required different handling, and inconsistent quality affected extraction reliability.
Manual Dimension Identification
Identifying dimensions with correct abbreviation suffixes demanded significant interpretation time. Engineers located target dimensions, interpreted abbreviation conventions, and extracted related tabular data manually.
Scale Constraints
Manual workflows could not handle peak volumes of 200 drawings per day without adding headcount. Processing capacity was constrained by available engineering hours.
Integration Friction
Extracted data needed structuring for ERP and planning workflows. Manual transcription into required formats introduced errors and delays in downstream processes.
The Approach / Methodology
Implementation moved from document analysis through pipeline configuration to production deployment. Each phase validated extraction accuracy before expanding to full processing volumes.
Document Analysis
Analysis of sample drawings across formats, identifying abbreviation conventions, layout patterns, and quality variations to inform extraction configuration.
Pipeline Configuration
Configuration of on-premise VLM and OCR pipeline for lift manufacturing drawing characteristics, including dimension formats and table structures.
Output Standardization
Design of two-sheet Excel templates (Drawing Dimensions and Tabular Data) matching downstream ERP and planning system requirements.
API Integration
Integration of real-time extraction API with existing engineering systems, enabling automated document submission and result retrieval.
Solution Implementation
Adeos deployed an on-premise pipeline combining vision language models with OCR. Drawings are processed through abbreviation-based filtering, table correlation, and confidence scoring. The result is structured Excel output from a single extraction pass, ready for ERP integration.
Hybrid Document Processing
On-premise VLM combined with OCR handles both digital AutoCAD PDFs and scanned client PDFs in a single pipeline, maintaining accuracy across document quality variations.
Intelligent Abbreviation Filtering
The system extracts only dimensions matching target suffixes (WW, WD, SS, HT), eliminating irrelevant data automatically and reducing downstream processing.
Table Correlation
Associated tabular data is extracted and linked to corresponding dimensions in one pass, preserving relationships between measurements and specifications.
Confidence-Scored API Output
Real-time API returns confidence scores with each extraction, enabling QC teams to focus review on lower-confidence results rather than checking everything uniformly.
Results
The deployment eliminated manual dimension interpretation as a bottleneck. Processing capacity scaled independently of engineering headcount, with confidence scoring enabling efficient quality control.
80-90% reduction in manual effort per drawing
Processing capacity scaled to 200 drawings per day at peak
Consistent two-sheet Excel output for every extraction
Both digital and scanned PDFs processed in single workflow
QC efficiency improved through confidence-based review prioritization
Direct integration with ERP and planning systems achieved
Key Learnings / Insights
This deployment reinforced principles for manufacturing document intelligence: hybrid processing for format variability, domain-specific configuration for relevance, and output design for adoption.
Hybrid Processing Handles Format Variability
Combining VLM and OCR in one pipeline addresses the full range of document quality without requiring separate workflows for digital and scanned sources.
Domain Configuration Beats Generic Extraction
Industry-specific abbreviation conventions (WW, WD, SS, HT) require explicit setup. Generic extraction returns too much irrelevant data to be useful.
Confidence Scoring Enables Scalable QC
Providing confidence scores with each extraction allows quality assurance to scale with volume through risk-based review rather than uniform checking.
Output Format Drives Adoption
Excel templates matching existing ERP requirements eliminated integration friction. The value of extraction is realized only when data flows into downstream workflows.
“Our engineers were spending hours on each drawing set, finding the right dimensions and typing them into spreadsheets. Now the system extracts exactly what we need, filtered by abbreviation, with confidence scores that tell us where to focus review. We process more drawings with fewer errors.”
Conclusion
The dimension extraction deployment demonstrates how Adeos handles high-volume manufacturing workflows with mixed document formats. By filtering extractions to relevant abbreviations and scoring confidence, the platform replaced manual interpretation with scalable, reliable automation. Engineering teams now focus on exceptions rather than routine extraction.
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