How this page was prepared: CyanBuild publishes this page and is one of the products discussed. The product facts below come from the linked vendor pages, each checked on October 29, 2026. No controlled, independent benchmark of these products was run for this page. The order is a reading order, not an accuracy or speed ranking, and published prices change — verify the current terms before you buy.
The honest answer first
If your drawing set is a scan, the useful question is not which tool is best. It is what your scans still contain. Below roughly 150 dpi the information is not in the file, and no amount of processing recovers it — you need to re-scan. Between about 150 and 300 dpi, most large, distinctive symbols survive. At 300 dpi a good scan is workable, but small symbols remain the weak point: in one construction-drawing benchmark, small-symbol performance scored 0.27 while large symbols scored 0.68, at the same 300 dpi and on the same sheets.
That asymmetry is the practical heart of this page. It is not that scans fail. It is that scans fail unevenly, and the failures concentrate on exactly the items a takeoff counts: the small repeated symbol, the fine partition line, the faint dimension.
What each input condition actually gives you
| Input condition | Typical resolution | What a detector can usually still read | What it usually cannot |
|---|---|---|---|
| Native vector PDF exported from CAD | Resolution-independent; linework stored as vector paths, annotation as text objects | Exact geometry: line endpoints, wall boundaries, opening positions and dimension text, with no pixel quantisation | Nothing in the linework — but a PDF can be mixed, so a vector title block over a raster plan means selectable text does not prove the linework is vector. |
| Whole-sheet rasterised PDF, a CAD print scanned at 300 dpi | 300 dpi; 14,042 × 9,934 px on an A1 sheet in the IJDAR dataset | Large, distinctive symbols, and dimension text at 10 pt or larger | Small symbols. In the IJDAR set, the small-symbol score was 0.27 for YOLO and 0.19 for Faster R-CNN even at 300 dpi, and anything whose shape resembles another class. |
| Good-quality office scan | 200–300 dpi | All 13 HVAC and plumbing symbol classes in the IJDAR set, and architectural symbols at an AP50 of 97.92 on the SESYD tile validation set | Small and repetitive classes. Window was the worst architectural class at an AP50 of 75.64, and V&C Provision scored an F1 of 0.00 in the construction set. |
| Archival or batch scan, common for 2000s-era paper sets | 100–150 dpi | Large geometry, room outlines and counts of large symbols | Fine detail merges into the background: door swing arcs, thin partition walls and small fixture symbols. Below about 150 dpi the information is not recoverable by interpolation, so upscaling cannot help. |
| Very low resolution, or a phone photo of a whole sheet | 15–75 dpi | Almost nothing reliable. In one study, GPT-4.1 transcription of 10 pt rendered text was 1.1% word-accurate at 15 dpi. | Everything. At 30 dpi or below, information is described as effectively destroyed by aliasing. |
| Skewed sheet, placed at an angle on the scanner bed | Any resolution | The geometry is still present in the file | Both line detection and OCR degrade, and skewed pages lead to inaccurate recognition. |
| Low contrast, faint linework or a faded print | Any resolution | Bold outlines only | Thin partition lines, light hatching and dimension lines. Low-contrast text produces poor OCR. |
| Bleed-through or show-through from the reverse side | Any resolution | Nothing that is reliably attributable to the front side | Spurious strokes from the reverse sheet become phantom walls and phantom dimensions. Binarisation cannot separate front ink from back ink. |
| Fold creases | Any resolution | Everything outside the crease band | Geometry within the crease, because the crease both loses line and adds a false edge. |
| Barrel distortion, from a flatbed of a bound set or a photographed sheet | Any resolution | All of the content, but the wrong shape | Lengths and areas. One vendor's illustrative arithmetic puts a line curving 4 mm across 20 m on an A1 plan at about 0.4 m of real-world error at 1:50 scale. |
| Handwriting, redlines or hand-marked-up sheets | Any resolution | The underlying printed geometry and printed annotation | The markup itself, which reads as extra walls, extra dimensions and extra notes. In a peer-reviewed test on a scanned 60% submittal with handwritten notes, the AI pulled dimension lines, arrows and notes into the quantities. |
| Dense annotation overlapping geometry, such as dimension strings over wall lines | Any resolution | The geometry, mostly | More false positives and more missed elements in the dense zones, because line detection and character shapes confuse each other. |
| Mixed scales and multiple viewports on one sheet | Any resolution | Each viewport's content, if calibrated individually | Anything measured with a single global scale. A site plan, a floor plan and an enlarged detail in one PDF can each use a different scale. |
| Low-contrast or faded title block | Any resolution; smaller text pushes the threshold up | Large title-block text | The revision, scale and sheet-index fields, which are exactly the fields you need to detect a revision mismatch. |
| A set assembled from scans of different resolutions | 100 dpi on some sheets, 300 dpi on others | Each page in isolation, at its own quality level | A single global resolution assumption. Per-page quality flags are needed, or the whole set has to be redone. |
Scan condition against what survives detection (checked October 29, 2026). The resolution numbers here come from three different kinds of source and must not be blended. Standards and authority figures are for documents and federal records, not for takeoff detection. The 200 and 150 dpi thresholds and the barrel-distortion and deskew figures are vendor blog claims with no published test set. The detection-accuracy columns come from benchmarks that each used one uniform resolution and never swept dpi, so no published study establishes an accuracy-against-resolution curve for takeoff. The cannot-read column is inference from those benchmarks and their stated failure modes, not a measured function.
The published numbers, and what they measure
The table below lists figures that were actually published, each with the scope of the measurement stated. That scope column is not decoration. Two symbol-detection benchmarks in this table differ by roughly ten times in mAP while describing superficially similar work, and the gap is entirely explained by one being industrial P&ID symbols and the other being architectural floor plans.
| Claim | Figure | What was actually measured | Source |
|---|---|---|---|
| Recommended scan resolution for reliable OCR of printed text | 300 dpi | General document OCR guidance from a commercial OCR vendor. Not a drawing benchmark and not a takeoff figure. | ABBYY source-image recommendations |
| Resolution below which image detail is lost | Below 150 dpi | Vendor OCR guidance, all document types. | ABBYY source-image recommendations |
| Resolution required for text below 10 pt | 400–600 dpi | Vendor OCR guidance for small fonts. | ABBYY small-text resolution guidance |
| Engine-level hard resolution bounds | Below 50 dpi or above 3200 dpi rejected | An input-validation pass/fail gate, not an accuracy goal. | ABBYY FlexiCapture Engine |
| Federal requirement for records with fine detail, explicitly including drawings | At least 392 ppi, being 400 ppi minus 2% | US federal records: photographic prints and paper records with fine detail, which the regulation lists as including drawings. Where the smallest significant detail is 1.0 mm or smaller, the resolution must be raised further. | 36 CFR 1236.50 |
| Information content at a given resolution | Resolution determines how much data is in the file but does not define or guarantee the quality of the information | A national archives digitisation quality guide, using a resolution test target. | NARA Digitization Quality Management Guide |
| Bicubic upscaling from a halved resolution | Halving 300 dpi and upscaling back gave slightly better recognition than the original 300 dpi images; results stay excellent down to about 150 dpi | Printed English text at 10 pt across roughly 1000 pages in varied fonts. Not drawings and not symbols. | arXiv 2105.04515 |
| Below that floor | Less than about 150 dpi offers much greater challenges because much less information is present in the image | The same peer-reviewed study, on general printed text. | arXiv 2105.04515 |
| OCR accuracy on ultra-low-resolution printed text | 60 dpi: 99.7% character and 98.9% word; 75 dpi: 99.9% character and 99.4% word | Roughly 1000 pages of printed English text in varied fonts, using a purpose-built end-to-end model. Not drawings. | arXiv 2105.04515 |
| GPT-4.1 word-level OCR accuracy by input resolution | 15 dpi 1.1%; 30 dpi 64.4%; 45 dpi 75.7%; 60 dpi 82.1%; 90 dpi 88.9%; 120 dpi 93.4%; 150 dpi 98.5%; 200 dpi 98.9%; 300 dpi 99.0% | Rendered text prompts transcribed at each resolution. This is text, not drawings — read it for the shape of the curve only. | arXiv 2605.07250 |
| Where information is destroyed rather than degraded | At 30 dpi and below, information is effectively destroyed by aliasing | The same rendered-text study, in the authors' own phase analysis. | arXiv 2605.07250 |
| Construction drawing symbol detection | mAP at 0.5 of 79% for YOLOv7 and 83% for Faster R-CNN | 13 HVAC and plumbing equipment symbol classes — valves, pumps, meters and ACVs — across 198 drawings rasterised at 300 dpi, with 665 symbols in the test set. Industrial construction drawings, not architectural floor plans. | IJDAR 2024, Springer |
| Detection rate on that same test set | 637 of 665 correct for YOLO and 636 of 665 for Faster R-CNN | The same test set. This is symbol recall, not takeoff quantity accuracy. | IJDAR 2024, Springer |
| How much of that headline depends on the threshold | AP across the full 0.5 to 0.95 range was 0.50 for both methods | The same test set, showing how much a loose intersection-over-union threshold inflates a headline number. | IJDAR 2024, Springer |
| Detection by symbol size at 300 dpi | Small symbols 0.27 for YOLO and 0.19 for Faster R-CNN; large symbols 0.68 and 0.64 | The same test set, using the COCO size definition of small as under 32 by 32 pixels. Most symbols in the set are small or medium. | IJDAR 2024, Springer |
| Time to analyse one construction drawing | 0.54 min for YOLO and 18.17 min for Faster R-CNN, with manual review of the AI output adding 1.20 min, against 1.34 hours for a subject-matter expert | One test drawing from the IJDAR set at 300 dpi. The 1.20-minute review addition is the number that matters for a scan-quality argument. | IJDAR 2024, Springer |
| Architectural floor plan symbol spotting on real drawings | mAP 59.03 with ResNet50 and 56.50 with Darknet19 | 115 real-world floor plan units from 10 architectural firms, PDFs converted to 150 dpi images, 12 architectural symbol classes. The mAP is roughly one tenth of the P&ID figures for a superficially similar task. | arXiv 2006.00684 |
| Best and worst architectural symbol classes | Window AP50 75.64, the lowest; toilet, oven and shower AP50 100.00 | The same real-world architectural floor plan test set. | arXiv 2006.00684 |
| Tiling is mandatory on large-format plans | Without tiling, mAP at 0.5 was 0.045; with 10% tile overlap, 0.972 | 118 architectural floor plans from 9 real-world building projects at 3500–5000 px on the short edge. A large-format floor plan cannot be processed in a single pass at all. | ISARC 2026 |
| Text recovery on the same drawings | Direct OCR on full drawings recovered 153 of 241 text instances; OCR on tiled drawings, 173; YOLO-based text detection followed by OCR, 233 | The same 118 architectural floor plans. Even at good resolution, direct OCR on the full sheet found only 63% of the text. | ISARC 2026 |
| Detection on genuinely scanned structural drawings | Grid reference precision 86.41% and recall 99.16%; horizontal beam precision 82.67% and recall 91.18% | 500 scanned 2D structural drawings of grid references, columns and beams at a fixed 850 × 750 px working resolution. Structural, not architectural, and not a takeoff. | Applied Sciences 10(6), 2066 |
| Effect of drawing density at fixed resolution | A column layout plan with 10 components scored 95.42% overall accuracy; with 25 components, 80.07% | 300 scanned structural test images. Crowding degrades accuracy at a fixed resolution, which is the same mechanism as lowering the resolution. | Applied Sciences 10(6), 2066 |
| Peer-reviewed evidence that AI degrades on scanned and hand-marked drawings | AI lacked accuracy on drawings of lower quality; on a scanned 60% submittal with handwritten notes it included dimension lines, arrows and notes in the quantities, omitted roof and upper-floor projections, and confused doors with furniture, windows and appliances | A masonry renovation project with a scanned drawing set and handwritten notes. | Togal.AI published case study |
| Manual against AI takeoff time on that study | About 71% average time savings across two case studies, with measurement accuracy within 5% for almost all classifications after manual adjustment | Fire station and multistory hotel architectural floor plans, a first-time user, On-Screen Takeoff against Togal.AI. The authors note a learning-curve effect and include a control case where most work was manual in both tools. | Togal.AI published case study |
| Vendor thresholds for automated detection on scans | 200 dpi minimum viable, about 150 dpi as a practical floor, and below 150 dpi automated takeoff is unreliable and the correction work offsets the time saving | The vendor's own processed job set, with no published test set, error-rate table or definition of accurate. | FOBECON vendor blog |
Published benchmark figures, with the scope of each measurement stated (checked October 29, 2026). Every figure was read on the linked page on October 29, 2026. Scope is the point of this table: an industrial P&ID valve benchmark and an architectural floor-plan benchmark are both symbol detection, but they differ by roughly ten times in mAP, and a resolution sweep on rendered text says nothing about detecting doors on a scan. Figures that are a vendor's own claim, or that were read second-hand inside another paper, are flagged in the scope column.
What to do with a set you already have
- Separate the good pages from the bad ones. Detect resolution per page and flag degraded pages. One OCR engine rejects anything below 50 dpi outright rather than guessing, which is the behaviour worth copying: refuse the page rather than produce a plausible wrong number.
- Check the title block before you measure. Revision, scale and sheet index are usually the first things lost on a faded scan, and they are exactly the fields you need to catch a revision mismatch.
- Calibrate each viewport separately. A sheet carrying a site plan, a floor plan and an enlarged detail can use a different scale in each. A single global scale will be wrong somewhere.
- Keep hand-marked sheets out of the machine path. Take the count from the clean issued sheet and carry the redlines as scope questions for the estimator.
- Budget the review time honestly. The IJDAR study measured 0.54 minutes of AI analysis, then 1.20 minutes of human review on top, against 1.34 hours for a subject-matter expert on the same drawing. The review is the number that decides whether the tool saves you anything.
What this page does not claim
No vendor accuracy figure is reproduced here as if it were a measured benchmark, and no comparison between two tools is drawn from figures measured on different tasks. No price appears, because none of the sources publishes one.
If you want the method rather than the failure modes, read how to count doors and windows on a floor plan and manual, digital and AI takeoff methods. For what practitioners say after using these tools, see the r/estimators verdict.
What does CyanBuild software cost?
These are CyanBuild’s published software plans as checked on October 6, 2026. Each allowance is for PDF pages per month. You review the quantities; an outsourced takeoff service is a separate purchase.
| Plan | Monthly price | PDF pages / month |
|---|---|---|
| Free | $0 | 3 |
| Starter | $29 | 30 |
| Pro | $99 | 120 |
| Enterprise | $299 | 400 |
Paid subscriptions renew according to their terms. A page allowance does not guarantee the completeness of a takeoff, and unused capacity does not turn the subscription into a per-project service. Check current features and terms before choosing a plan.
What else should you check?
- What resolution do I need for a scanned drawing?
- For general OCR of printed text, 300 dpi is the standard recommendation and below 150 dpi detail is lost. For drawings specifically, a US federal records regulation covering paper records with fine detail, explicitly including drawings, requires at least 392 ppi. That is a records-management standard rather than a takeoff benchmark, but it is the only published figure that names drawings. As a practical floor, one vendor states 200 dpi as minimum viable and 150 dpi as the point where automated takeoff becomes unreliable.
- Can I just upscale a low-resolution scan to improve the result?
- Below about 150 dpi, no. One peer-reviewed study on printed text found that below that threshold much less information is present in the image, and that halving 300 dpi and upscaling back actually gave slightly better recognition than the original 300 dpi images. Upscaling helps between roughly 150 and 300 dpi. Below 150 dpi the pixels do not contain the information, so re-scan instead.
- What is the difference between a vector PDF and a scanned one?
- A native CAD export stores linework as vector paths and annotation as text objects, so endpoints, boundaries and dimension text are exact and measurable with no pixel quantisation. A scan stores pixels, so every measurement is a resolution question. But a PDF can be mixed: a vector title block over a raster plan means selectable text does not prove the linework is vector. Zoom to 400–800% and look for pixelation to check.
- What is the worst thing that happens on a hand-marked sheet?
- The markup is read as geometry. In a peer-reviewed test on a scanned 60% submittal with handwritten notes, the AI pulled dimension lines, arrows and notes into the quantities, omitted roof and upper-floor projections, and confused doors with furniture, windows and appliances. On paper, treat a hand-marked sheet as an estimator input rather than a machine input: take the count from the clean issued sheet and carry the redlines as scope questions.
- Do these benchmark numbers apply to my floor plan?
- Often not, and the table says which ones do not. The strongest construction-drawing figures — around 79–83% mAP — come from HVAC and plumbing symbol classes on industrial drawings, not architectural floor plans. The architectural floor-plan benchmarks are roughly ten times lower in mAP, around 56–59%, on 150 dpi real-world drawings. And the scanned-structural figures measure grid references and beams, not takeoffs. Read the scope column before quoting any of them.
- What should I do with a mixed-quality set?
- Detect the resolution per page and flag degraded pages rather than assuming one setting for the whole set. One OCR engine rejects pages below 50 dpi outright rather than guessing, which is the right behaviour to copy. Then accept the good pages and spend your review time only on the flagged ones.
Where do the figures and product facts come from?
Sources checked October 29, 2026. Published prices can change; verify the actual terms before purchasing. Worked examples on this page are labeled separately from published product or service facts.
- CyanBuild published plans
Monthly software prices and PDF page allowances. These are product terms, not market averages or outsourced service fees.
- ABBYY source-image recommendations
The 300 dpi recommendation, the 150 dpi floor, the 400–600 dpi small-text figure, and the scanner defaults. Checked October 29, 2026.
- 36 CFR 1236.50 — federal records with fine detail
The 294 ppi and 392 ppi requirements, the latter covering records with fine detail that explicitly include drawings. Checked October 29, 2026.
- NARA Digitization Quality Management Guide
That resolution determines how much data a raster file holds but does not define or guarantee the quality of the information. Checked October 29, 2026.
- Gilbey & Schönlieb — low-resolution text recognition
The 150 dpi information floor, the bicubic upscaling finding, and the ultra-low-resolution OCR accuracies. Checked October 29, 2026.
- Song et al. — GPT-4.1 OCR accuracy by input resolution
The per-resolution OCR accuracy curve and the aliasing phase boundary at 30 dpi. Checked October 29, 2026.
- Jamieson, Moreno-Garcia and Elyan — IJDAR 2024 construction drawing detection
The mAP, recall, threshold-sensitivity, symbol-size and time-to-analyse figures on 198 construction drawings at 300 dpi. Checked October 29, 2026.
- Real-world floor plan symbol spotting
The architectural floor-plan mAP figures, the per-class results, and the SESYD synthetic benchmark. Checked October 29, 2026.
- ISARC 2026 — large-format architectural floor plan detection
The tiling requirement and its effect on accuracy, and the text-recovery comparison. Checked October 29, 2026.
- Applied Sciences — detection on scanned structural drawings
The scanned-drawing precision and recall figures and the density effect at fixed resolution. Checked October 29, 2026.
- Togal.AI peer-reviewed study against On-Screen Takeoff
The 71% time-saving finding and the documented failure modes on scanned, hand-marked drawings. Checked October 29, 2026.
- FOBECON — PDF plan quality and AI accuracy
The vendor's 200 dpi and 150 dpi thresholds, the deskew tolerance and the barrel-distortion arithmetic. Checked October 29, 2026.
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