pdf

name	pdf
description	Use when working with PDF files. Covers text and table extraction, merging and splitting, form filling, watermarking, OCR for scanned documents, and generating PDFs.
metadata	{"category":"documents","version":"1.0.0","tags":["pdf","extraction","ocr","forms","generation"]}

PDF

Purpose

Read, manipulate, and produce PDFs — including the scanned ones that contain no text at all and the ones whose tables are drawn rather than structured.

When to Use

• Extracting text or tables from a PDF.
• Merging, splitting, or rotating pages.
• Filling a PDF form.
• OCR on a scanned document.
• Generating a PDF report or invoice.

Capabilities

• Text extraction, with layout preservation where it matters.
• Table extraction.
• Page operations: merge, split, rotate, reorder, watermark.
• Form field reading and filling.
• OCR for scanned or image-only PDFs.
• Generation with proper typography and pagination.

Inputs

• The source PDF, and whether it contains real text or images of text.
• The target: extracted data, a modified PDF, or a new one.

Outputs

• Extracted text or structured tables.
• A valid modified or generated PDF.

Workflow

1. Determine whether it has text at all — Extract from the first page. If nothing comes out, it is a scan, and every text-based approach will silently return nothing. That is the single most important check.
2. Choose the extractor by the task — Simple text extraction for prose; a layout-aware extractor for anything where column position carries meaning; a dedicated table extractor for tables.
3. OCR only when necessary — It is slow and imperfect. If the PDF has a text layer, use it.
4. Preserve what matters when editing — Merging PDFs drops bookmarks, form fields, and annotations unless you carry them across deliberately.
5. Verify the output — Open it. A PDF that a library writes without error can still be structurally broken.

Best Practices

• A PDF that returns an empty string from text extraction is not empty; it is a scan. Check for this before concluding the file is corrupt.
• Tables in PDFs are usually drawn lines and positioned text, not structured tables. A general text extractor will interleave the columns into nonsense. Use a table-specific tool.
• Text extraction order follows the PDF's internal content stream, not the visual reading order. A two-column layout will frequently extract as interleaved lines.
• OCR quality depends overwhelmingly on the input image. Deskew and increase the contrast before OCR; it is worth more than any tuning of the OCR engine.
• Do not use pypdf for text extraction quality — it is fine for page operations and poor at text. Use pdfplumber or PyMuPDF.
• A generated PDF needs embedded fonts, or it will render differently on every machine that lacks them.

Examples

The check that must come first:

import pdfplumber

with pdfplumber.open("document.pdf") as pdf:
    first_page_text = pdf.pages[0].extract_text() or ""

if len(first_page_text.strip()) < 50:
    # This is a scan. Every text-based approach will return nothing, silently.
    # It is not a corrupt file and it is not an empty document.
    text = ocr_pdf("document.pdf")
else:
    text = "\n".join((p.extract_text() or "") for p in pdf.pages)

Table extraction, which text extraction cannot do:

with pdfplumber.open("invoice.pdf") as pdf:
    for page in pdf.pages:
        # A general text extractor turns this into interleaved gibberish, because
        # the "table" is drawn lines and absolutely-positioned text.
        for table in page.extract_tables():
            header, *rows = table
            df = pd.DataFrame(rows, columns=[h.strip() if h else "" for h in header])
            yield df

OCR with preprocessing, which matters more than the OCR settings:

import fitz            # PyMuPDF
import pytesseract
from PIL import Image, ImageOps

def ocr_pdf(path: str, dpi: int = 300) -> str:
    doc = fitz.open(path)
    pages = []

    for page in doc:
        # 300 DPI is the practical minimum for reliable OCR. 150 halves the accuracy.
        pix = page.get_pixmap(dpi=dpi)
        img = Image.frombytes("RGB", (pix.width, pix.height), pix.samples)

        # Preprocessing is worth more than any OCR engine parameter.
        img = ImageOps.grayscale(img)
        img = ImageOps.autocontrast(img)

        pages.append(pytesseract.image_to_string(img, config="--psm 6"))

    return "\n\n".join(pages)

Notes

• --psm 6 tells Tesseract to assume a uniform block of text, which is correct for most scanned documents and substantially more accurate than the default automatic page segmentation on a clean scan.
• PyMuPDF (fitz) is significantly faster than the alternatives for rendering and text extraction, and its licence (AGPL) matters for commercial use — check before adopting it.
• A PDF form's fields are named, and the names are frequently not what the visible labels say. Enumerate the fields before filling them.