ocr-and-documents

Extract text from scanned PDFs and images using OCR by applying marker-pdf for OCR-enabled processing of complex layouts, tables, and forms. This handles scanned documents natively, converting them into searchable and editable text for downstream archiving workflows.

The best way to get text from a native PDF is using pymupdf for lightweight and fast text extraction. This bypasses heavier OCR processing, directly parsing the embedded text to output plain text or Markdown formats for immediate analysis.

Yes, you can extract text directly from a remote PDF URL by using the web_extract function first. This automatically fetches and processes online documents to obtain text without requiring a local download, falling back to local extractors if needed.

Yes, this PDF text extraction approach handles tables and complex layouts by utilizing marker-pdf. It performs OCR-enabled extraction specifically designed to parse structured elements like tables and forms, outputting structured formats including JSON with metadata.

When extracting text from PDFs and scans, you can generate flexible outputs including Markdown, plain text, or structured formats like JSON with metadata. This optionally includes extracted images and tables to integrate directly into research pipelines.

You should avoid using marker-pdf for document text extraction when processing native, digitally-generated PDFs that already contain embedded text. In those cases, relying on pymupdf provides a faster, lightweight extraction without the unnecessary overhead of OCR.