ocr-and-documents

PyMuPDF handles lightweight, fast text extraction from native PDFs with selectable text. For scanned documents and images requiring OCR, the pipeline applies marker-pdf to achieve high-accuracy recognition and convert non-selectable text into editable content.

Yes, you can extract tables and metadata alongside main text. The script-based outputs generate markdown, plain text, and structured metadata, enabling direct integration into research notes, contract digitization, or archival data retrieval pipelines.

You convert a PDF to markdown by providing the document and specifying markdown as the output format. The extraction pipeline processes native PDFs and OCR-processed scans, yielding structured markdown suitable for academic research and archiving.

The best way to digitize scanned documents for searchable content is using high-accuracy OCR via marker-pdf. This process converts non-selectable text from image-based scans into editable, searchable formats like markdown and plain text.

Yes, the PDF extraction pipeline supports batch processing and URL-based inputs. You can choose fast local parsing for native PDFs or robust OCR for difficult layouts, extracting text directly from local files or URLs.

Lightweight extraction via pymupdf is fast but limited to native PDFs with selectable text. OCR via marker-pdf handles scanned documents and difficult layouts but requires more processing, making it slower for simple digitization tasks.