OCR 文字识别

这款工具的作用

A scanned PDF is a picture of a document, so searching it finds nothing and selecting a word selects nothing. This renders each page, recognises the words on it, and draws them back over the image invisibly, so the page looks as it did and the words are suddenly there. Recognised pages are rebuilt from that rendering: they come out grayscale at the resolution you chose, and any links, form fields or annotations they carried do not survive. Pages that already have real text are left alone, byte for byte.

Reach for it whenever a document arrived as pictures rather than words: a contract someone scanned and emailed, a receipt photographed on a phone, a fifty-page report from an office machine, or an old paper you need to quote from without retyping it.

工作原理

1. Drop the scan onto this page. PDFs work, and so do JPEG, PNG, WebP and TIFF images - an image becomes a one-page PDF first.
2. Choose the language. English and Indonesian are installed, and the combined mode recognises both at once in a document that mixes them.
3. Leave the mode on only pages without text unless you know for certain that every page is a scan.
4. Set the resolution if you need to. 300 dpi suits ordinary print; go towards 400 for very small type, or down towards 150 for a long document on a slow machine.
5. Choose whether you want the searchable PDF, a plain text file, or both, then press Run OCR.

Recognition costs something on the pages it touches, and it is better to know before than after. Each recognised page is rendered to a grayscale image at your chosen resolution, and the new page is that image with the invisible words drawn over it. Colour on those pages is gone, and their links, form fields and annotations are not carried across. Skipped pages are copied untouched. The file usually grows, sometimes a lot, because a 300 dpi page image is bigger than the compressed scan it replaced - Compress PDF is the normal second step.

The words have to land in the right place or a text layer is worse than none. The recogniser returns each word's box in the pixels of the rendered image, counting down from the top, while a PDF page counts up from the bottom, so every box is scaled by the render ratio and flipped. Words scored below 40 out of 100 are left out entirely: a confidently wrong word sends search to the wrong page, while a gap only sends it nowhere.

Nothing is uploaded, and that includes the recogniser itself. tesseract.js fetches its WebAssembly core and its language data from a third-party CDN unless told otherwise, which would have meant your scan quietly announcing itself to a third party the first time anyone here ran OCR, so all of it is served from this site instead. The cost is a one-off download. The benefit is that recognition involves nobody else at all, which you are welcome to check in the network tab.

Resolution decides both how accurate the reading is and how long you wait for it. Below about 200 dpi, thin serifs and small print break up and accuracy falls away quickly. Above 300 the gains get small while render time and output size keep climbing. 300 is the default because that is roughly where the curve flattens for ordinary body text.