7 Tips to Get More Accurate OCR Results

The fastest way to get more accurate OCR is to fix the photo, not hunt for a different tool: keep the shot in focus, well-lit, straight-on, and cropped tight to the text. Do those four things and most recognition errors disappear before the model even runs.

Most OCR mistakes are not model failures — they are image problems. A modern recognition engine like PP-OCRv6 is good at reading clear text; it is bad at guessing what a blurry, tilted, or badly lit photo was probably trying to say. Fix the input and the output usually fixes itself. Here are the adjustments that matter most, roughly in order of how often they are the actual problem.

1. Get the text in focus

Blur is the single biggest killer of accuracy. If you are photographing a document rather than screenshotting it, hold the camera steady, let it autofocus on the text before you shoot, and avoid motion blur from shooting while walking or in low light. A slightly out-of-focus photo often still looks fine at a glance on your phone’s small screen and only reveals the problem once the model starts guessing at characters that are technically just a smear of gray pixels.

2. Use enough resolution

Text needs to be legible at the pixel level, not just to your eye. A tiny, heavily compressed thumbnail — the kind you might screenshot out of a video call or a slide deck — often loses the fine detail that separates similar characters, like a lowercase “l” from a capital “I,” or a “5” from an “S” in a condensed font. When you have a choice, capture at a higher resolution rather than scaling a small image up — upscaling adds pixels but not the detail that was never captured in the first place.

3. Improve lighting and contrast

Even, diffuse light beats a single harsh source that throws shadows across the page. Avoid glare from glossy paper or screens, and make sure the text is meaningfully darker (or lighter) than its background — low-contrast text on a busy or similarly-toned background is genuinely hard for any OCR engine to separate from noise. A receipt printed on thermal paper that has started to fade is a good real example: the contrast loss that makes it hard for your own eyes to read is the same thing that trips up the model.

4. Straighten the shot

A page photographed at a steep angle distorts letter shapes and spacing — perspective makes the far edge of the page smaller and the lines subtly non-parallel, which is exactly the kind of distortion a recognition model was not trained to expect. Shoot as close to straight-on as you can, directly above the document rather than from the side.

5. Crop out the noise

Backgrounds, other objects, and unrelated text in the frame give the detector more to sort through and more chances to pick up something you did not want — a second, smaller receipt underneath the one you meant to scan, for instance. Cropping tightly to the text you care about before running recognition improves both speed and accuracy.

6. Flatten curved or folded pages

Photos of open books or folded documents curve the lines of text near the spine or crease, which distorts letter shapes exactly where a camera captures them worst. Press the page as flat as practical before shooting, or photograph one side at a time.

7. Match the source to the right tool

A single photo or screenshot works well in Extract Text from Image. A multi-page scanned document does better run through a page-by-page PDF workflow instead of stitching pages into one giant image. Using the tool built for your input format avoids a whole category of avoidable accuracy loss. If your source is specifically a screenshot, pasting it straight from the clipboard — as covered in how to extract text from a screenshot — also skips an extra save-and-reopen step that can quietly recompress the image before OCR ever sees it.

None of this requires special equipment — a phone camera and a bit of care with light and focus gets you most of the way there. The rest is the model’s job, and the two-stage detection-then-recognition pipeline behind it is worth understanding if you are curious why some of these fixes work the way they do — see how browser-based OCR actually works.