Optical Character Recognition (OCR) is the process of detecting individual characters inside an image and converting them back into real, selectable, copyable text. It's a genuinely different task from "converting" an image the way a format converter does — there's no source text data to preserve, because an image never had text in it to begin with, just pixels arranged in shapes that happen to look like letters to a human eye. The engine has to recognize each shape as a specific character, in context, from scratch.
What's Actually Happening
This tool runs on an open-source OCR engine (Tesseract) executed locally on our own servers via a WebAssembly build — not a third-party API. It analyses an image region by region, identifies shapes that resemble known characters, and reassembles them into words and lines based on their position and spacing. Nothing about this process "reads" the image the way a person does — it's pattern-matching against a trained character model, which is exactly why the specific visual quality of your source image matters so much to the result.
What Genuinely Affects Accuracy — Tested, Not Assumed
It's tempting to assume that generic image-enhancement tricks — sharpening, upscaling, boosting contrast — would help OCR read a harder image. We tested that assumption directly rather than take it on faith: on a genuinely degraded sample image (small text, slightly blurred, heavily compressed — the kind of image OCR struggles with most), running it through upscaling, grayscale conversion, contrast normalization, and sharpening in sequence made the result worse at every step, not better. Confidence dropped from 40% on the raw image down to 23% after all four "enhancements" were applied.
The reason: sharpening and upscaling don't recover detail that was never captured in the first place — a blurred letter is blurred because the information about its true edges is gone, not hidden. Sharpening a blurry image just amplifies the blur's soft edges and any JPEG compression artifacts into harder, more distinct — but still wrong — shapes, which confuses the character model more than the original soft version did.
| Factor | What we found |
|---|---|
| Text size in the image | Small text is genuinely harder — there's less pixel data per character to begin with. This can't be fixed after the fact by upscaling; it needs to not be small in the source. |
| Blur | Meaningfully hurts accuracy, and — per our test — can't be reliably corrected by sharpening after the fact. A sharp source image matters more than any post-processing trick. |
| JPEG compression | Heavy compression introduces blocky artifacts that OCR can mistake for character strokes. A less-compressed source (or a PNG screenshot instead of a re-saved JPEG) genuinely helps. |
| Contrast | Very low contrast (light gray text on a near-white background) does hurt recognition, but artificially boosting contrast after the fact didn't recover accuracy in our test — it's better to start with reasonably high-contrast text than to try to fix low contrast afterward. |
| Handwriting vs. printed text | This engine is tuned for printed and typed text. Handwriting recognition is a different, harder problem with its own dedicated models — expect noticeably lower accuracy here regardless of image quality. |
Getting the Best Result
- Use the highest-resolution version of the image you have — don't screenshot a screenshot, or crop and re-save a small region as a new lower-res file
- Prefer PNG screenshots over JPEG photos of a screen when possible — JPEG's compression artifacts are one of the harder problems for OCR to see past
- If photographing a physical document, use good even lighting and hold the camera parallel to the page to avoid perspective distortion and shadows
- Straight-on, unrotated, in-focus source images consistently outperform anything that needs correcting after the fact — there's no reliable substitute for a genuinely clear source
Using the Tool
- Go to Image to Text (OCR)
- Upload a JPG, PNG, WebP, BMP, or TIFF image containing text (up to 25 MB)
- Wait a few seconds while the engine processes it server-side
- Copy the extracted text directly, or download it as a plain .txt file
When the Result Comes Back Wrong or Empty
| What you see | Likely cause |
|---|---|
| Garbled, nonsense characters | Source image is too small, blurred, or heavily compressed — see the factors above. Try a clearer, higher-resolution source rather than the same image again. |
| No text detected at all | Text may be too small, too low-contrast, or the image may not actually contain machine-printed text (e.g. it's handwriting or a stylized logo) |
| Some words right, others wrong | Normal for mixed-quality images — parts of the source were clearer than others. Cropping to just the relevant text region before uploading sometimes helps since the engine has less irrelevant area to reason about. |
Your uploaded image is deleted from our servers immediately after processing — nothing is stored, and the extracted text is returned directly to you, never saved in a database.