Extract text from PDF files
Extract named entities from text
Process and extract text from images
Find relevant text chunks from documents based on queries
Find relevant passages in documents using semantic search
Fetch contextualized answers from uploaded documents
Analyze PDFs and extract detailed text content
Extract named entities from medical text
Extract text from images using OCR
Search and summarize documents with natural language queries
OCR for Arabic Language with QR code and Barcode Detection
Find information using text queries
Upload and analyze documents for text extraction and Q&A
Pymupdf Pdf Data Extraction is a powerful tool designed to extract text and data from PDF files, including scanned documents. It leverages OCR (Optical Character Recognition) technology to accurately retrieve text from image-based PDFs, making it a versatile solution for document processing.
• OCR Support: Extracts text from scanned PDFs and images with high accuracy.
• Comprehensive Extraction: Retrieves text, layouts, and formatting from PDF documents.
• Multi-Column Handling: Identifies and extracts text from multi-column layouts.
• Page-Specific Extraction: Allows extraction of text from specific pages or the entire document.
• File Flexibility: Supports encrypted PDF files and works with both text-based and scanned PDFs.
pip install pymupdf to install the library.import fitz to import the library in your Python script.doc = fitz.open("file.pdf").page_text = doc.load_page(pagenumber).get_text().doc.close().Is Pymupdf suitable for extracting text from scanned PDFs?
Yes, Pymupdf supports OCR and can extract text from scanned PDFs with high accuracy.
How do I handle encrypted PDF files?
Encrypted PDFs can be opened by providing the correct password during the fitz.open() process.
Can I extract text from specific pages only?
Yes, Pymupdf allows you to load and extract text from specific pages using doc.load_page(pagenumber).