Because Countfire’s automatic counting algorithm is necessarily "fuzzy", to allow it to automatically count lots of similar symbols, in certain scenarios you can run into false positives.

These scenarios include:

While false positives can be unexpected when you first encounter them, they're a normal part of using Countfire and are easy to correct.

Text like elements

Because text like elements don’t include the additional metadata that real text does, Countfire’s automatic counting algorithm can’t use that additional information to help differentiate between symbols.

In most cases this isn’t an issue, however, with certain symbols, it can increase the chances of running into false positives.

However, it’s important to note that given enough information (selections), Countfire can always tell the difference between two symbols.

In the clip above, an initial selection of a Type C symbol is made. Because the text within the Type C symbol is only a text like element (not real text), this selection causes the Type B and Type D symbols to be misidentified (false positives).

These false positives happen for two reasons:

  1. From the software’s perspective, The Type B, C & D symbols are similar.
  2. Countfire hasn’t yet been shown what a Type B or D symbol is.

These false positives are easily corrected by making additional selections to give Countfire more information to work with.

Additionally, if you didn’t pick up these false positives while making selections within Countfire, they’re easy to spot when reviewing the check sheets at the end of the project.

Check sheet with false positives

Check sheet without false positives

Learn more about reviewing the check sheets.

Very simple selections

If your selections are very simple (EG: just 1 or 2 elements), the chances of false positives is increased.

In the clip above, a selection of just the letter "A" is made (not including the symbol's body), and because there are many occurrences of the letter "A" throughout the drawing, a number of false positives are generated within the notes.

Then, a second selection is made, including both the symbol's body and its "A" reference, and because that selection is more specific, no false positives are generated.

Learn more

How to: Handle PDFs that are actually images.

Did this answer your question?