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Measuring the value

Searching a scanned drawing set: 171 pages recognised in 338 seconds, and the paging hours that come back

Has a measurement By 3 Nov 2026 About 9 minutes
Eight drawing sheets: three with their own text, five scanned images (hatched). The search box says M250 and the hits land on scanned sheets too. No real data. M250 7 hits · 4 from scans own text scanned 171 scanned pages made searchable: 338 s text pages and scans counted separately
Eight drawing sheets, five of them scans: the search box finds hits on pages with text and on scanned pages alike.

In a drawing set, a scanned page yields nothing to Ctrl+F; looking up a mark means paging. Once every scanned page is recognised into text — text pages and scans counted separately, an interrupted run resuming where it stopped — how many hours a month does an estimator get back, and does the largest set even finish?

How many hours a month does an image-only drawing set cost an estimator?

All it needs is the sets per month, the pages in a set, the share of pages that are scans and an hourly cost. Lookups, the time to page through every scan and today's hours follow from those. Everything is editable.

Sets you have to read to take off, check or review
Pages with their own text and scanned pages together
Pages where Ctrl+F finds nothing at all
Salary plus social insurance, per hour

Derived from the four numbers above

Scanned pages a month–
Lookups a month–
Paging through one set's scans–
Lookup + index hours today–
Assumptions — open to see and edit each one

Lookups

Sheet index

To subtract

Share of lookups not yet found, by minute Solid: today · dashed: once scans carry text · shaded: the minutes saved
Share of lookups not yet found, by minute

A. Lookups on scanned pages–
B. Sheet index–
C. Waiting for and checking recognition–
Hours given back each month, valued–

An estimate, not a promise. The share read correctly is the assumption every line stands on: set it to 0 and A and B go to zero, leaving only the subtraction. An honest model has to be able to do that.

A design set reaches an estimator as a PDF of a few hundred pages, and a good share of those pages are scanned images: signed and stamped sheets photographed again, or old prints scanned for the archive. On those pages Ctrl+F finds nothing. To learn where the footing concrete grade is written, what section column C1 has, or which sheet carries the note on cover, people page through.

This piece asks: if every scanned page were recognised into text and searchable like any other page, how many hours a month would an engineer get back? And a question that comes first: does the recognition finish the whole set at all?

1. What changes

When a drawing set is uploaded, the machine separates the two kinds of page and counts them separately: pages with their own text (read straight from the file) and pages that are scanned images (which have to be recognised). The status line speaks for the whole file, for example "312 pages: 267 with their own text, 45 scanned — 45 recognised in 58 seconds". It used to say only "45 pages recognised", which readers took to mean the machine had stopped at page 45.

Then:

  1. Every scanned page is recognised, Vietnamese and English, on the server's CPU. Each search hit from a scanned page is marked "OCR" with its confidence, so the user knows to look at the drawing before trusting it.
  2. Sheet number and title read from a scanned title block go into the drawing index, as for pages with text.
  3. An interrupted run does not start over. Results are saved every 10 pages, so a rerun reads only the missing pages. Tested: stopped at page 30 of the 171-page set, the rerun read only the remaining 141.
  4. Big sets upload in 8 MB pieces. The delivery network in front of the server refuses any request over 100 MB and the browser shows nothing: a 117 MB set once crawled at 5 KB/s for 18 minutes. Now a piece that hits a network error is sent again on its own, and sending it again never creates a duplicate file.
Searching 220x220 in a sample drawing set: 10 hits, all on scanned pages, each labelled OCR 96%; the open sheet KC-07 is labelled as a scan with OCR text
A 16-page sample drawing set, 10 pages of it scanned. Typing "220x220" finds 10 places, all on scanned pages, each showing the confidence of the recognition; a click opens the right sheet with the text highlighted. Sample data, not from any real project. The interface is in Vietnamese.

2. The question that comes first: does it finish?

Recognising one scanned A3 page takes between 1 and 53 seconds on its own. A 171-page set, measured on a development machine with 6 processes in parallel: 338 seconds, about 2 seconds a page.

On the real server — 4 cores, shared with other systems — the same set at first did not finish: it stopped at a 300-second limit. The measured cause: 6 processes running at once, each opening one thread per core, so 24 threads fighting over 4 cores. Once each process was held to one thread and the parallel count set to cores minus one, the same 171 pages on that same machine finished in 352 seconds.

That matters more than any number below. A set that never finishes recognition is not "slower to search" — nothing in it can be found, and every task that needs to look something up in it stops or goes back to paging by hand. The calculator below counts only the hours on sets that did finish; the work that stops entirely is not in it, so the total is a floor.

3. First stream: lookups on scanned pages

Minutes saved per lookup = share of scanned pages × share of words read correctly × (time to page through every scan ÷ 2 − minutes per search)

A word on a page with its own text is found with the search box today. On a scanned page it has to be paged to, and on average it turns up halfway. Once scans carry text, the share read correctly moves onto the search step; the share read wrongly still has to be paged to. The chart above is exactly this arithmetic: the shaded area between the two curves is the minutes saved per lookup.

Example A 171-page set, 60% scanned: 103 pages. Scanning one A3 page for a word takes 6 seconds, so paging through all of them is 10.3 minutes; on average 5.1 minutes a lookup. Searching by text, reading the hits and opening the page: 0.5 minutes. The machine reads 80% of looked-for words correctly. Each lookup saves 0.6 × 0.8 × (5.1 − 0.5) ≈ 2.2 minutes. 60 lookups a set, 4 sets a month: 240 × 2.2 ÷ 60 ≈ 8.9 hours a month.

How to measure it. For one week, every time you have to find something in a drawing set, make a mark and time it. Count separately the lookups that ended on a page with text and on a scan. Measure the share read correctly on your own set: take 20 words whose position you know, search each, count how many land on the right page.

How far to trust it: fair. Lookups and seconds per page differ a lot between someone who knows the set and someone opening it for the first time. The share read correctly depends on the scan — skewed, blurred, or with a red stamp across the text, it drops sharply.

4. Second stream: the drawing index

Hours saved a month = scanned pages a month × share read correctly × (minutes to write one sheet by hand − minutes to check one already read) ÷ 60
Example 4 sets × 103 scanned pages = 410 pages a month. Writing one scanned sheet's number and title into the index by hand: 0.5 minutes. Once it has been read from the title block, what is left is checking: 0.1 minutes. A sheet read wrongly is still written by hand. 410 × 0.8 × (0.5 − 0.1) ÷ 60 ≈ 2.2 hours a month.

How to measure it. Next time you build a drawing index, time it over 20 scanned sheets. Then count on the tool how many sheets came out with the right number and needed no correction.

How far to trust it: fair. Every design office has its own title block; an unfamiliar one is read correctly less often.

5. To subtract: waiting and checking

Recognising a 171-page set takes about 6 minutes on the server, in the background, with no AI model fees — this part uses text-recognition software on the CPU. The user still spends time: waiting, rechecking pages flagged as failed, pressing run again. Count 10 minutes a set: 4 × 10 ÷ 60 ≈ 0.7 hours a month.

6. Adding it up

StreamHours/monthMoney/month (VND 80,000/h)
Lookups on scanned pages8.90.71 m
Drawing index2.20.18 m
Waiting and checking−0.7−0.05 m
Given back10.40.83 m

About 10 hours a month, close to VND 10 million a year at the salary in the example, for an engineer reading 4 sets a month. Today lookups and the index on those sets take about 16.5 hours.

Counted once. Those 10 hours are money or capacity to take on more sets, never both. And as section 2 said, the figure leaves out the sets in which nobody can look anything up today.

7. What this ROI stands on

The share of words read correctly. It sits under both streams. Set it to 0 in the calculator: lookups and the index go to zero and only the subtraction is left. On the sample set in the screenshot, clear print came out at 96% confidence; on real scans, handwriting, text under a stamp, or rebar notation such as Ø is often read wrongly. That is why every hit from a scanned page is marked "OCR" with its confidence — a reminder to look at the drawing, not decoration.

Not found does not mean not there. A word the machine misread will not show up in the hits. For things where missing one costs money — a material note, a detail that contradicts the plan — the engineer still has to know the set; the tool shortens the way to the page, it does not replace the reading.

Lookups per set is the number easiest to overstate. Measure a week before you believe 60.

8. Measuring it in your own company

  1. Take a baseline. Pick a set you are working on. Count scanned pages against all pages; for a week, log lookups and minutes per lookup; time the index over 20 scanned sheets.
  2. Run the same set on the tool. Note the recognition time, the failed pages, and the share read correctly on 20 words whose position you know.
  3. Compare like with like. A structural set full of rebar notation differs from an architectural set full of words; compare structural with structural.

9. What to remember

A page you cannot search is a page you read again by eye every time you need it. Recognition does not make anyone read drawings faster — it makes getting to the right page cost nothing. And before counting savings, ask a blunter question: does your largest set actually finish?

A drawing set that cannot be searched is not slower — it stops.

The recognition times (338 seconds for 171 pages on a development machine, 352 seconds on a 4-core server), the stop at the 300-second limit before the fix, the 312-page set with 267 text pages and 45 scans, and the 117 MB set uploading at 5 KB/s were measured on the Xây dựng G20 tool with real design sets, anonymised. The screenshot comes from a sample project. Sets per month, share of scans, lookups, seconds per page, share read correctly and salary are illustrative. Replace them with your own.

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