2nd/10_Wiki/Topics/DevOps_and_Security/PDF-Format.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-pdf-format	PDF Format	10_Wiki/Topics	verified	self	Portable Document Format ISO 32000 PDF/A	none	A	0.9	applied	pdf document format parsing generation		2026-05-10	pending	language framework python pypdf

PDF Format

매 한 줄

"매 cross-reference table 의 random-access 의 binary container". 매 Adobe (1993) 의 PostScript-derived 의 ISO 32000 의 standardize 의 page-fixed-layout 의 dominant interchange format. 매 2026 년 의 PDF/A-4 (archival) + PDF 2.0 의 modern variant 의 LLM-extraction 의 challenge 의 source (no semantic structure 의 guarantee).

매 핵심

매 file 구조

1. Header — %PDF-2.0 (또는 1.x).
2. Body — sequence of indirect objects (N G obj ... endobj).
3. Cross-reference table (xref) — byte offset of each object.
4. Trailer — root + info + size + xref offset.

매 object types

• Boolean, Number, String (literal () or hex <>), Name (/Name), Array, Dictionary, Stream (filtered binary).
• Page tree (Catalog → Pages → Page) + Resources (Font, XObject, etc.).

매 응용

1. Text/table extraction (LLM 의 RAG ingest).
2. Form fill (AcroForm / XFA).
3. Digital signature (PAdES).
4. Print fidelity (PDF/X for press).
5. Archive (PDF/A — embed fonts, no encryption).

💻 패턴

Text extraction (pypdf, 2026)

from pypdf import PdfReader

reader = PdfReader("doc.pdf")
text = ""
for page in reader.pages:
    text += page.extract_text() + "\n"
# pypdf 5.x: layout-mode option for column-aware
text = "\n".join(p.extract_text(extraction_mode="layout") for p in reader.pages)

Better extraction with pdfplumber (preserves layout)

import pdfplumber

with pdfplumber.open("doc.pdf") as pdf:
    for page in pdf.pages:
        # Tables
        for table in page.extract_tables():
            print(table)
        # Words with bbox
        for word in page.extract_words():
            print(word['text'], word['x0'], word['top'])

LLM-grade extraction with Unstructured (2026)

from unstructured.partition.pdf import partition_pdf

elements = partition_pdf(
    filename="doc.pdf",
    strategy="hi_res",          # uses layout model
    infer_table_structure=True,
    extract_images_in_pdf=True,
)
# Each element: Title, NarrativeText, Table, Image

Generate PDF (reportlab)

from reportlab.lib.pagesizes import A4
from reportlab.pdfgen import canvas

c = canvas.Canvas("out.pdf", pagesize=A4)
c.setFont("Helvetica-Bold", 16)
c.drawString(72, 800, "Invoice #1234")
c.setFont("Helvetica", 10)
for i, line in enumerate(items):
    c.drawString(72, 760 - i*14, line)
c.showPage()
c.save()

Modern HTML→PDF (Playwright, replaces wkhtmltopdf)

from playwright.async_api import async_playwright

async def html_to_pdf(html, out):
    async with async_playwright() as p:
        browser = await p.chromium.launch()
        page = await browser.new_page()
        await page.set_content(html)
        await page.pdf(path=out, format="A4", print_background=True)
        await browser.close()

Sign PDF (PAdES, pyhanko)

from pyhanko.sign import signers, fields
from pyhanko.pdf_utils.incremental_writer import IncrementalPdfFileWriter

with open("input.pdf", "rb") as inf:
    w = IncrementalPdfFileWriter(inf)
    fields.append_signature_field(w, sig_field_spec=fields.SigFieldSpec("Sig1", box=(50, 50, 200, 100)))
    signer = signers.SimpleSigner.load("cert.pem", "key.pem")
    with open("signed.pdf", "wb") as out:
        signers.sign_pdf(w, signers.PdfSignatureMetadata(field_name="Sig1"), signer=signer, output=out)

Repair / linearize (qpdf CLI)

qpdf --linearize input.pdf output.pdf
qpdf --object-streams=generate --compress-streams=y input.pdf small.pdf
qpdf --check input.pdf            # validate xref + structure

Encrypted PDF

from pypdf import PdfWriter

writer = PdfWriter(clone_from="doc.pdf")
writer.encrypt(user_password="user", owner_password="owner", algorithm="AES-256")
with open("encrypted.pdf", "wb") as f:
    writer.write(f)

매 결정 기준

상황	Tool
Text extraction (simple)	pypdf 5.x
Layout / tables	pdfplumber
LLM RAG ingest	Unstructured + hi_res / Marker / Docling
Generation (reports)	reportlab / WeasyPrint
HTML → PDF (modern)	Playwright (Chrome headless)
Forms / signing	pyhanko + qpdf
Repair / optimize	qpdf, mutool

기본값: 매 ingest → Unstructured (layout-aware), 매 generate → Playwright (HTML).

🔗 Graph

• 응용: Document AI
• Adjacent: OCR

🤖 LLM 활용

언제: 매 form-filled PDF 의 question. 매 extraction tool 의 selection. 매 schema mapping. 언제 X: 매 binary blob 의 direct edit 의 LLM 의 X. 매 spec-conformant tool 의 use.

❌ 안티패턴

• Regex-based PDF parsing: 매 binary + xref 의 fragile. 매 lib 의 사용.
• Single extraction strategy: 매 scanned PDF 의 OCR fallback. 매 hi_res strategy.
• No PDF/A for archive: 매 font 의 missing 의 future render fail.

🧪 검증 / 중복

• Verified (ISO 32000-2:2020, pypdf docs, Unstructured docs, qpdf manual).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — PDF structure + 2026 extraction/generation toolchain