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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →For a mixed PDF corpus, extract native text page by page first, then OCR a rendered image of the same page only when its text layer yields no usable text. Keep every result attached to the original PDF page number and record whether it came from native extraction or OCR. That per-page hybrid approach is a design recommendation—not a schema imposed by PDF.js or Tesseract.js—and the fallback rule should be tested against your documents.
How to extract native PDF text by page in Node.js
PDF.js’s Node example loads a PDF, reads its page count, and requests each page’s text content. Its documented flow imports pdfjs-dist/legacy/build/pdf.mjs, opens the document with getDocument, loops from page 1 through numPages, and calls getPage(i) followed by getTextContent(). Text items expose a str value that the example maps into extracted text. See PDF.js’s Node text-extraction example.
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The page-by-page API gives your application a natural place to retain source-page provenance. The example does not prescribe an index schema, define usable text, or set a fallback threshold; those are application decisions. Test extraction on representative pages, including sparse, garbled, or otherwise unsuitable results, before deciding when to invoke OCR.
When to OCR a PDF page
Tesseract.js does not process PDF files directly. Its documented route is to render PDF pages to images using a separate library, then pass those images to Tesseract.js for recognition. In Node.js, supported image inputs can include a local file path or a buffer. See the Tesseract.js FAQ and its image-format documentation.
#1 Best Overall
Use OCR for a page when its native extraction has no useful text—not simply because the page looks scanned. A scanned-looking page may still contain an embedded text layer, while a born-digital page can have text extraction problems. The useful-text test and the fallback threshold must reflect the PDFs and languages your application handles.
Keep each extracted result owned by its original page
Store native text and OCR text against the source PDF page from which they came. A practical record should identify the source document, the original one-based page number, the extracted text, and the extraction method. This is an application design recommendation based on the documented page-scoped extraction and image-OCR workflows; neither project requires this particular record shape.
Rank #2
- Source identity: enough information to locate the original PDF.
- Original page number: the PDF’s one-based page number, suitable for citations and navigation.
- Text: the extracted or recognized content for that page.
- Method: native extraction or OCR, so downstream code can distinguish their provenance.
PDF.js’s example passes one-based page numbers to getPage. If your index uses a zero-based array offset, convert it explicitly at the API boundary and retain the original PDF page number in the stored record. PDF.js’s viewer documentation also frames navigation by page number; see the viewer page concept.
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Choose a per-page hybrid, then validate it
For documents mixing selectable text and scans, a per-page hybrid is the most defensible starting point: use native extraction when the page produces usable text and render-plus-OCR when it does not. It avoids OCR where usable native text already exists while providing a path for image-only pages. This is an implementation inference from the documented APIs, not a guarantee that native extraction is always correct or that every scanned page lacks text.
Rank #3
| Decision factor | What to check |
|---|---|
| Coverage | Does every page produce usable text through the chosen path? |
| Traceability | Can every result be tied back to its source document and original page? |
| Input condition | Does the page contain embedded/selectable text, page imagery, or a mixture? |
| Fidelity | Are reading order, characters, language, layout, and scan quality acceptable on representative pages? |
| Throughput and resource cost | Measure native extraction, rendering, and OCR on your actual workload; the cited documentation establishes no universal comparative benchmark. |
| Operational complexity | Account for rendering dependencies, OCR language data, worker lifecycle, and output normalization. |
The Tesseract.js FAQ says Scribe.js can extract text-native PDFs significantly faster and more accurately than running OCR. That is a project FAQ’s statement about that library and workflow, not a controlled benchmark for every PDF, OCR engine, Node deployment, or workload. Use representative documents to make your own comparison rather than treating it as a general performance guarantee.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Manage OCR workers and normalize output
For multiple image-recognition jobs, the Tesseract.js README recommends creating one worker, reusing it for the jobs, and terminating it when the batch is finished. This is lifecycle guidance, not a measured performance guarantee.
Rank #4
If you use Tesseract’s PDF output mode, its documented output retains page imagery with a hidden searchable text layer. That format is useful when the desired artifact is a searchable PDF rather than a database index. For plain-text output, Tesseract inserts a form-feed character after each page by default; account for that delimiter when parsing its text. See the Tesseract FAQ.
Quick Recap
What to test before indexing a corpus
- Compare extracted text with the visible page on samples of selectable-text, scanned, mixed, sparse, and problematic pages.
- Define what qualifies as usable native text, including how your application handles empty, garbled, or incomplete results.
- Check that OCR output remains attached to the same original page that was rendered.
- Verify page-number conversion if internal arrays or database keys are zero-based.
- Measure fidelity, throughput, and resource use on your own documents and deployment; the cited sources provide no universal comparison figures.
- Check the documentation for your installed package releases, since APIs and package behavior can change.
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