How to Improve OCR Accuracy When Extracting Text from Images

When OCR gives you garbled text, the instinct is to switch tools. But most of the time, the tool is not the problem — the image is. The same image that produces garbage output in one pass will extract cleanly after a few simple preparation steps. Here is what actually improves OCR accuracy, in order of impact.

1. Resolution: The Most Important Factor

OCR engines need enough pixels to distinguish characters. Below 150 DPI, small text starts to break down. Below 100 DPI, accuracy drops sharply. The sweet spot for reliable OCR is 300 DPI or higher.

In practice:

• Phone camera photos taken close-up at standard settings are usually 300+ DPI — generally fine
• Screenshots from modern devices (2x/3x Retina) are excellent — very high effective resolution
• Compressed or resized images from web pages may be too low-res — download the original if possible
• Photos taken far away from the document will have insufficient resolution — retake closer

Rule of thumb: characters should be at least 20-30 pixels tall in the image. If individual letters look blurry at 100% zoom, the image needs improvement before OCR.

2. Contrast and Lighting

Dark text on white background extracts almost perfectly. Problems start when:

• Shadow falls across part of the page (common with phone photos of documents)
• Background and text have similar brightness (grey text on light grey)
• Glare reflects off glossy paper, washing out text
• Colored backgrounds reduce the text-to-background contrast

Fix: Before OCR, use your phone's built-in photo editor or a free tool to increase contrast and brightness. Even a quick auto-enhance often makes a significant difference. For phone photos of printed documents, use the "Document" scan mode in your camera app if available — it flattens perspective and improves contrast automatically.

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3. Image Orientation and Skew

OCR works best on horizontal text. Rotated or skewed images — even slightly — reduce accuracy. A 5-10 degree tilt is enough to cause notable errors.

Fix: Straighten the image before uploading. Most photo editors have a straighten or rotate tool. On iPhone, tap Edit in Photos and use the crop/rotate control. On Android, the Gallery editor has the same option.

For strongly tilted or warped document photos (curved pages from books, perspective distortion from an angle), you will get better results using a dedicated document scanner app that corrects perspective before exporting the image.

4. Language Selection

This is the most overlooked accuracy factor. If you are extracting Spanish text with English selected, the OCR engine treats accented characters as anomalies and often misreads them.

Always match the language setting to the language in the image. The Image to Text tool supports 8 languages — select the correct one before clicking Extract. This alone can turn garbled accented text into clean output.

5. What Cannot Be Fixed by Prep

Some accuracy problems are not solvable with image prep:

• Handwriting — especially cursive or informal writing. Printed text always wins.
• Decorative or script fonts — stylized typefaces that look like handwriting confuse OCR engines
• Very small text — fine print at 6pt or smaller may not extract cleanly regardless of resolution
• Text over complex backgrounds — watermarks, patterns, or photos behind text increase error rates

For these cases, manual transcription or a specialized AI-powered OCR service may be needed.

Frequently Asked Questions

Why does OCR miss some words in my image?

Common causes: low resolution in that area of the image, shadow or glare obscuring the text, text close to the image edge getting cropped, or a font the OCR engine has difficulty with. Improve image quality first, then re-extract.

Does image file format (JPG vs PNG) affect accuracy?

Slightly. JPG compression can introduce artifacts around sharp edges (like text), which confuses OCR. PNG is lossless and generally gives marginally better OCR results for screenshots and scanned documents. For photos, the quality difference is minimal.

What DPI should I scan documents at for OCR?

300 DPI is the standard recommendation for reliable OCR. 600 DPI provides better results for small fonts or complex characters. Scanning above 600 DPI rarely improves accuracy meaningfully and produces unnecessarily large files.

Can I improve results by cropping to just the text area?

Yes. Cropping out irrelevant image content (borders, margins, other objects) helps by focusing the OCR on the text region. It also speeds up processing.