Words to check · oneocr cookbook

Use calibrated confidence to find the words a person should look at, on a grainy, tilted copy of the receipt.

A word’s Confidence is the calibrated probability that the word is exactly right, fitted per script on held-out data, so one threshold means the same thing on every page. receipt-photo.jpg is the sample receipt turned 3°, scaled down, blurred, given grain and saved as a low-quality JPEG:

using OneImage;
using OneOcr;
 
OcrEngine engine = OcrEngine.LoadBundled();
OcrPage page = Image.Load("receipt-photo.jpg").Ocr(engine);
Console.WriteLine($"straightened {page.SkewDegrees:F1}°, {page.Words.Count} words");
 
foreach (OcrLine line in page.Blocks.SelectMany(b => b.Lines))
    Console.WriteLine($"{line.Words.Min(w => w.Confidence),5:P0}  {line.Text}");
 
IEnumerable<OcrWord> check = page.Words.Where(w => w.Confidence < 0.9f);
Console.WriteLine($"Below 90%: {string.Join(" ", check.Select(w => $"\"{w.Text}\" {w.Confidence:P0}"))}");

Text

straightened -3.0°, 48 words
   4%  1
   3%  e
   3%  3
  99%  Corner Market
  95%  1402 Oak Street
  99%  78704 Austin
  95%  +1 512 555 0142
  98%  Date: 24/10/2026
  13%  : Time: 14:32
  98%  Coffee beans
  96%  2x Oat milk
 100%  Bagels
   3%  15
  98%  12.99
  98%  8.98
  99%  5.25
  10%  g	Subtotal	27.22
   7%  U2	Tax 8.25 %	2.07
  98%  TOTAL	$ 29.29
  65%  d**** 4821
  98%  Paid by	Card
 100%  Thank you!
   3%  9N
Below 90%: "1" 4% "e" 3% "3" 3% ":" 13% "15" 3% "g" 10% "U2" 7% "d****" 65% "9N" 3%

The reader straightened the 3° tilt, and every word of the receipt’s own text scores 90% or more with one exception: the payment line came back as two lines, and **** was misread as d****, at 65%.
The grain was read as a few short “words” (1, e, 15, 9N and others). Each scores 13% or less, so the threshold separates them from the receipt’s text.
RawConfidence is the uncalibrated mean of the characters’ best probabilities; use Confidence for thresholds. Lines and blocks average their words’.
Coming from Tesseract? Its conf is a 0–100 score; divide an existing threshold by 100 as a starting point, then look at the words just below it (Moving from Tesseract).