ocr-fallback

To extract text from scanned PDFs when primary tools are slow, you can use fallback OCR methods that leverage pymupdf for image extraction and easyocr or pytesseract for text recognition. This approach provides efficient text extraction on resource-constrained systems.

The best way to perform OCR on scanned PDFs with limited resources is using a fallback method with pymupdf to extract page images, followed by easyocr or pytesseract for text recognition, avoiding the overhead of heavier primary OCR tools.

Yes, this fallback OCR method works with both easyocr and pytesseract. It utilizes pymupdf to extract pages as images from scanned PDFs, then processes those images using either easyocr or pytesseract to extract the text.

You should use a fallback OCR method instead of marker-pdf when marker-pdf is unavailable, too slow, or when your system lacks sufficient resources. It is specifically designed for administrative tasks requiring OCR on resource-constrained systems.

Pymupdf handles image extraction from scanned PDFs by extracting individual pages as images. These extracted images are then passed to easyocr or pytesseract to perform the actual text recognition, providing an efficient alternative OCR pipeline.

Yes, you can use pytesseract to extract text from PDFs containing scanned images. The process utilizes pymupdf to first extract the scanned pages as images, which are then processed by pytesseract to recognize and extract the text.