Text Extraction

OmniDocs provides several extractors for extracting raw text from PDFs and images. Each extractor is implemented as a class with a consistent interface, making it easy to switch between methods for different document types.

Available Text Extractors

• PyMuPDFTextExtractor: Fast, reliable text extraction from PDFs using PyMuPDF.
• PdfplumberTextExtractor: Lightweight PDF text extraction with layout awareness.
• PyPDF2TextExtractor: Simple PDF text extraction using PyPDF2.
• PdftextTextExtractor: General PDF text extraction backend.
• SuryaTextExtractor: Deep learning-based text extraction for scanned or complex documents.
• DoclingTextExtractor: Advanced parsing and text extraction for structured documents.

Tutorials

• Text Extractors

Basic Usage Example

from omnidocs.tasks.text_extraction.extractors.pymupdf import PyMuPDFExtractor

# Initialize extractor
extractor = PyMuPDFExtractor()

# Extract text from PDF
result = extractor.extract("path/to/file.pdf")

# Access text
print(result.text)

Batch Processing Multiple PDFs

import os
from omnidocs.tasks.text_extraction.extractors.pdfplumber import PDFPlumberExtractor

extractor = PDFPlumberExtractor()
pdf_folder = "path/to/pdf_folder/"

for filename in os.listdir(pdf_folder):
    if filename.lower().endswith(".pdf"):
        pdf_path = os.path.join(pdf_folder, filename)
        try:
            result = extractor.extract(pdf_path)
            print(f"{filename}: {len(result.text)} characters extracted")
        except Exception as e:
            print(f"Error processing {filename}: {e}")

Working with Different Extractors

from omnidocs.tasks.text_extraction.extractors import (
    PyMuPDFTextExtractor, PdfplumberTextExtractor, PyPDF2TextExtractor,
    PdftextTextExtractor, SuryaTextExtractor, DoclingTextExtractor
)

extractors = [
    PyMuPDFTextExtractor(),
    PdfplumberTextExtractor(),
    PyPDF2TextExtractor(),
    PdftextTextExtractor(),
    SuryaTextExtractor(),
    DoclingTextExtractor()
]
pdf_path = "sample.pdf"

for extractor in extractors:
    try:
        result = extractor.extract(pdf_path)
        print(f"{extractor.__class__.__name__}: {len(result.text)} characters")
    except Exception as e:
        print(f"{extractor.__class__.__name__} error: {e}")

Output Format

All extractors return a TextOutput object with:

class TextOutput:
    text: str           # Extracted text content
    source_file: str    # Path to the processed file

Installation Requirements

Each extractor may require specific dependencies:

PyMuPDFTextExtractor

pip install pymupdf

PdfplumberTextExtractor

pip install pdfplumber

PyPDF2TextExtractor

pip install pypdf2

PdftextTextExtractor

pip install pdfminer.six

SuryaTextExtractor

pip install surya-ocr torch

DoclingTextExtractor

pip install docling-tools

Troubleshooting

1. No Text Detected: - Try a different extractor. - Check if the PDF is scanned (use OCR extractors if so).

2. Import Errors: - Install missing dependencies as shown above.

3. Output Not as Expected: - Inspect the extracted text and try another extractor for better results.

Best Practices

1. Choose the Right Extractor:
2. Use PyMuPDF for fast, general-purpose extraction.
3. Use PDFPlumber for layout-aware extraction.

4. Use PyPDF2 for simple, lightweight extraction.

5. Optimize Performance:

6. Batch process files and initialize extractors once.

7. Handle Errors Gracefully:

8. Wrap extraction in try-except blocks.

9. Log or print errors for debugging.

10. Validate Output:

11. Always inspect the extracted text for completeness and accuracy.