Technology

Scanned Bank Statement OCR: Getting Clean Data from Images

· 6 min read

Branch-printed statements, emailed scans and phone photographs all arrive as images. There is no text layer to select, so extraction begins with optical character recognition. Understanding that step makes it much easier to judge the output.

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What OCR actually does

OCR detects character shapes in an image and converts them into text, along with the position of each character on the page. Table structure is then inferred from those positions — column boundaries from horizontal alignment, rows from vertical grouping.

What degrades accuracy

Most OCR errors trace back to the quality of the source image.

  • Low resolution scans below roughly 200 DPI.
  • Skewed or rotated pages from a phone camera.
  • Shadows, glare and creases across the amount column.
  • Faint thermal or dot-matrix printing.

Digits are where errors matter

A misread letter in a description is cosmetic. A misread digit in an amount is a reconciliation problem. This is why numeric validation is more useful than any headline accuracy percentage.

Every conversion runs a balance check: the opening balance plus the sum of all extracted transactions must equal the closing balance printed on the statement. The result is shown before you download, so a mis-read figure surfaces at conversion time rather than at review.

Improving the source before you upload

If you control the scan, use 300 DPI, greyscale rather than colour, and a flat page. If the client controls it, asking for the bank's original PDF instead of a scan removes the OCR step entirely.

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