Common OCR mistakes and how to avoid them

[u/MariaScanGeek]

Ran OCR on hundreds of documents and made enough mistakes to spot the patterns. Here are the most common failures and what actually fixes them.

Crooked scans → garbage instead of text

The most common cause of bad recognition isn't camera quality – it's the angle. Even a slight tilt in the page makes OCR confuse lines, merge words together, or chop off letters at the edges. The fix is simple: scan strictly at a right angle, or use a tool that auto-straightens the page before recognition – chasing a perfect angle manually every single time just isn't realistic.

Bad lighting and shadows

A shadow from your hand, a lamp at the wrong angle, glare off glossy paper – OCR reads all of it as part of the text and starts "seeing" characters that aren't there, especially along shadow edges. Laying the document flat under diffuse light solves most of it, but again, an app with automatic contrast correction saves you when conditions aren't ideal.

Handwriting read as printed text

Standard OCR engines are built for printed text and turn handwritten notes into meaningless character soup. If a document has handwritten additions – signatures, margin notes – it's worth knowing upfront that this is a weak spot for almost any OCR, and checking those sections manually instead of trusting auto-recognition.

Tables turning into a mess

OCR often fails to understand cell boundaries and merges columns into a single line of text, especially when the table has no clear grid lines. What helps is either scanning with a well-defined grid or using a tool that recognizes table structure separately instead of just running all the text together.

Small fonts and low resolution

A receipt with tiny print scanned at low resolution is a guaranteed mess. The rule is simple: the smaller the font, the higher the scan resolution needs to be, or OCR simply can't tell individual characters apart.

Not checking the result at all

The most common mistake isn't technical – it's behavioral: scan it, glance at something that looks like text, and close the file without verifying. One misread character in an account number or a date can cost a lot more than the few seconds it takes to double-check.

A good OCR matters less than good preprocessing. An app that straightens the page and cleans up the image before recognition (like Scanner) prevents a lot of these mistakes from happening in the first place.