The pipeline from PDF to structured data
The drawing-to-data pipeline is the foundation of AI-assisted quantity takeoff. It takes a construction PDF drawing — a flat, rasterised image with no inherent structure — and converts it into structured data that can be measured, queried, and compiled into a BOQ.
Here is the end-to-end pipeline:
- PDF upload and pre-processing — Convert the PDF page into a high-resolution image. Detect drawing boundaries, title blocks, and revision panels. Identify drawing scale from the title block or scale bar.
- Layout analysis — Determine what type of drawing this is (plan, section, elevation, detail). Identify the drawing area versus borders, notes, and legends. On multi-drawing sheets, separate individual views.
- Text extraction — Read all text on the drawing: room labels, dimensions, annotations, keynotes, grid references, level markers, material notes. Associate each text element with its position on the drawing.
- Annotation recognition — Interpret non-text drawing elements: dimension lines, hatching patterns, symbols (door swings, electrical outlets, drainage), leader lines, and section cut markers.
- Spatial understanding — Identify rooms, walls, openings, and elements. Understand the spatial relationships: this room is bounded by these walls, this door is in this wall, this dimension describes this span.
- Structured output — Produce a JSON or structured data representation of the drawing content: rooms with areas, walls with lengths and thicknesses, openings with sizes and types, annotations with their referenced elements.
Each step has its own tools, challenges, and failure modes. Let us work through them.
