Python Bot for Structural Drawings
Budget: $30 – $250 USD
I want a Python-based bot that can open a set of structural, civil and architectural construction drawings in PDF or DWG format, understand what it “sees,” and then let me treat those sheets as an interactive knowledge guide.
Here is what I need it to do once a drawing set is loaded:
• Automatically extract all relevant measurements—spans, beam depths, column sizes, slab thickness, rebar counts, and any other dimensional data it can detect.
• Identify materials and finishes shown in the details, with concrete recognition taking priority over everything else.
• Detect typical design elements such as doors, windows, openings, and any structural annotations that influence concrete work.
• Allow me to ask follow-up questions focused on concrete (for example, “How much concrete volume is required for Grid B-C?” or “Show rebar schedule for Footing F4”) and answer them based on the extracted data.
You are free to lean on computer-vision and machine-learning tools—OpenCV, PyTorch, TensorFlow, Tesseract OCR, or whatever combination you think is most efficient—as long as the final solution runs in standard Python, preferably 3.9+.
Deliverables I expect:
• A fully working Python program or package with clear, step-by-step setup instructions
• Any trained models or weights used, plus a short note on how they were trained
• Sample output generated from one of my test drawings, demonstrating measurement extraction, material detection (showing concrete flagged), element recognition, and a Q&A exchange about concrete quantities
• Brief documentation (markdown is fine) describing the architecture so I can maintain and extend it later.
If something in the spec is unclear, feel free to suggest the best technical approach before you start coding.
Here is what I need it to do once a drawing set is loaded:
• Automatically extract all relevant measurements—spans, beam depths, column sizes, slab thickness, rebar counts, and any other dimensional data it can detect.
• Identify materials and finishes shown in the details, with concrete recognition taking priority over everything else.
• Detect typical design elements such as doors, windows, openings, and any structural annotations that influence concrete work.
• Allow me to ask follow-up questions focused on concrete (for example, “How much concrete volume is required for Grid B-C?” or “Show rebar schedule for Footing F4”) and answer them based on the extracted data.
You are free to lean on computer-vision and machine-learning tools—OpenCV, PyTorch, TensorFlow, Tesseract OCR, or whatever combination you think is most efficient—as long as the final solution runs in standard Python, preferably 3.9+.
Deliverables I expect:
• A fully working Python program or package with clear, step-by-step setup instructions
• Any trained models or weights used, plus a short note on how they were trained
• Sample output generated from one of my test drawings, demonstrating measurement extraction, material detection (showing concrete flagged), element recognition, and a Q&A exchange about concrete quantities
• Brief documentation (markdown is fine) describing the architecture so I can maintain and extend it later.
If something in the spec is unclear, feel free to suggest the best technical approach before you start coding.