ExtractGrid uses high-quality text recognition models for OCR, but image resolution still has a direct impact on accuracy. Even the best models can misread characters when the source image lacks enough detail.
How resolution affects OCR
OCR works by identifying the shape of individual characters. When an image is low resolution, letters become blurry, edges soften, and similar characters—such as 0 and O, or 1 and l—are harder to distinguish. Small font sizes and faint text are especially vulnerable.
This is not a limitation unique to ExtractGrid. It is a general constraint of optical character recognition: the model can only read what the image clearly shows.
Common signs of a resolution problem
- Random or missing characters in the extracted text
- Numbers converted incorrectly
- Words broken apart or merged together
- Poor results on fine print, stamps, or handwritten notes
How to improve OCR results
- Scan at higher resolution — Aim for at least 300 DPI where possible.
- Use sharp, well-lit source documents — Avoid shadows, glare, and heavy compression.
- Prefer native PDF text when available — For digital PDFs with embedded text, use direct text extraction instead of OCR.
- Re-export or re-scan problem files — If a batch file looks pixelated, replace it with a clearer version before processing.
Summary
ExtractGrid is built to deliver strong OCR performance, but input quality remains essential. Using clear, high-resolution images is the most effective way to reduce recognition errors and get dependable extracted data.