pdf-inspector

Fast PDF classification and text extraction. Detects whether a PDF is text-based or scanned, extracts text with position awareness, and converts to clean Markdown — all without OCR. Python bindings via PyO3 for the pdf-inspector Rust library.

Built by Firecrawl to handle text-based PDFs locally in under 200ms, skipping expensive OCR services for the ~54% of PDFs that don't need them.

Features

Smart classification — text_based / scanned / image_based / mixed in ~10–50ms, with a confidence score and per-page OCR routing.
Markdown conversion — headings, lists, code blocks, bold/italic, URL linking, and dual-mode table detection (PDF drawing ops + text-alignment heuristics).
Layout-aware extraction — multi-column reading order, position and font info per text item, RTL support.
Robust text decoding — CID/Type0 fonts via ToUnicode CMaps, plus automatic flagging of broken encodings so callers can fall back to OCR.
Lightweight — native Rust core, no ML models, no external services; ships type stubs.

Benchmark

opendataloader-bench corpus (200 PDFs), local engines without model-based PDF parsing; OCR disabled. Scores 0–1, higher is better:

Engine	Overall	Reading order	Tables (TEDS)	Headings	Speed
pdf-inspector	0.875	0.915	0.814	0.788	0.470s
liteparse	0.873	0.913	0.693	0.811	0.750s
opendataloader	0.831	0.902	0.489	0.739	2.569s
pymupdf4llm	0.735	0.886	0.401	0.424	17.117s
markitdown	0.589	0.844	0.273	0.000	16.165s

Refreshed July 31, 2026, on Apple M4 Pro; speed is the median of five complete corpus runs after an excluded warm-up. Full methodology and versions are in the repo README, with raw timings and artifacts in the results branch.

Install

pip install pdf-inspector

Prebuilt wheels cover CPython ≥3.8 on Linux (x86_64, aarch64), macOS (Intel, Apple Silicon), and Windows (x64). Other platforms build from source, which requires a Rust toolchain. For local development in a repo checkout:

pip install maturin
maturin develop --release

Usage

import pdf_inspector

# Full processing: detect + extract + convert to Markdown
result = pdf_inspector.process_pdf("document.pdf")
print(result.pdf_type)      # "text_based", "scanned", "image_based", "mixed"
print(result.confidence)     # 0.0 - 1.0
print(result.page_count)     # number of pages
print(result.markdown)       # Markdown string or None

# Process specific pages only
result = pdf_inspector.process_pdf("document.pdf", pages=[1, 3, 5])

# Process from bytes (no filesystem needed)
with open("document.pdf", "rb") as f:
    result = pdf_inspector.process_pdf_bytes(f.read())

# Fast detection only (no text extraction)
result = pdf_inspector.detect_pdf("document.pdf")
if result.pdf_type == "text_based":
    print("Can extract locally!")
else:
    print(f"Pages needing OCR: {result.pages_needing_ocr}")

# Plain text extraction
text = pdf_inspector.extract_text("document.pdf")

# Positioned text items with font info
items = pdf_inspector.extract_text_with_positions("document.pdf")
for item in items[:5]:
    print(f"'{item.text}' at ({item.x:.0f}, {item.y:.0f}) size={item.font_size}")

# Per-page markdown (one Markdown string per page, plus layout metadata)
result = pdf_inspector.extract_pages_markdown("document.pdf")
for page in result.pages:
    print(f"Page {page.page}: {len(page.markdown)} chars, needs_ocr={page.needs_ocr}")

# Restrict to specific 0-indexed pages (preserves caller order)
result = pdf_inspector.extract_pages_markdown("document.pdf", pages=[0, 2])

# Structure-tree elements from tagged PDFs (empty list when untagged).
# Pages are 1-indexed to match TextItem.page, so (page, mcid) joins directly
# against extract_text_with_positions — e.g. to recover real heading levels:
elements = pdf_inspector.extract_structure_elements("tagged.pdf")
roles = {(e.page, e.mcid): e.role for e in elements}
headings = [
    item.text
    for item in pdf_inspector.extract_text_with_positions("tagged.pdf")
    if item.mcid is not None and roles.get((item.page, item.mcid), "").startswith("H")
]

API reference

Function	Description
`process_pdf(path, pages=None)`	Full processing (detect + extract + markdown)
`process_pdf_bytes(data, pages=None)`	Full processing from bytes
`detect_pdf(path)`	Fast detection only (returns PdfResult)
`detect_pdf_bytes(data)`	Fast detection from bytes
`classify_pdf(path)`	Lightweight classification (returns PdfClassification)
`classify_pdf_bytes(data)`	Lightweight classification from bytes
`extract_text(path)`	Plain text extraction
`extract_text_bytes(data)`	Plain text extraction from bytes
`extract_text_with_positions(path, pages=None)`	Text with X/Y coords and font info
`extract_text_with_positions_bytes(data, pages=None)`	Text with positions from bytes
`extract_text_in_regions(path, page_regions)`	Extract text in bounding-box regions
`extract_text_in_regions_bytes(data, page_regions)`	Region extraction from bytes
`extract_pages_markdown(path, pages=None)`	Per-page Markdown + layout metadata (all pages by default)
`extract_pages_markdown_bytes(data, pages=None)`	Per-page Markdown from bytes
`extract_structure_elements(path, pages=None)`	Structure-tree elements from tagged PDFs (page, mcid, role)
`extract_structure_elements_bytes(data, pages=None)`	Structure-tree elements from bytes

Types

Type stubs (pdf_inspector.pyi) ship with the package. Result types at a glance:

class PdfResult:                     # process_pdf / detect_pdf
    pdf_type: str                    # "text_based" | "scanned" | "image_based" | "mixed"
    markdown: str | None             # extracted Markdown (None for detect_pdf)
    page_count: int
    processing_time_ms: int
    pages_needing_ocr: list[int]     # 1-indexed
    ocr_reasons_by_page: list[PageOcrReasons]
    title: str | None
    confidence: float                # 0.0 - 1.0
    is_complex_layout: bool
    pages_with_tables: list[int]
    pages_with_columns: list[int]
    has_encoding_issues: bool        # broken font encodings — consider OCR fallback

class PageOcrReasons:                # per-page OCR diagnostics
    page: int                        # 1-indexed
    reasons: list[str]               # machine-readable reason identifiers

class PdfClassification:             # classify_pdf
    pdf_type: str
    page_count: int
    pages_needing_ocr: list[int]     # 0-indexed
    confidence: float

class TextItem:                      # extract_text_with_positions
    text: str
    x: float
    y: float
    width: float
    height: float
    font: str
    font_size: float
    page: int
    is_bold: bool
    is_italic: bool
    is_underline: bool
    is_strikeout: bool
    item_type: str
    mcid: int | None                 # marked-content ID for tagged PDFs (None otherwise)

class StructureElement:              # extract_structure_elements
    page: int                        # 1-indexed (matches TextItem.page)
    mcid: int
    role: str                        # "H1".."H6", "P", "Table", ... (resolved via /RoleMap)

class RegionText:                    # extract_text_in_regions
    text: str
    needs_ocr: bool
    ocr_reason: str | None           # machine-readable OCR reason

class PageRegionTexts:               # extract_text_in_regions
    page: int                        # 0-indexed
    regions: list[RegionText]

class PagesExtractionResult:         # extract_pages_markdown
    pages: list[PageMarkdown]        # PageMarkdown: page (0-indexed), markdown, needs_ocr, ocr_reason
    pages_with_tables: list[int]     # 1-indexed
    pages_with_columns: list[int]    # 1-indexed
    pages_needing_ocr: list[int]     # 1-indexed
    ocr_reasons_by_page: list[PageOcrReasons]
    is_complex: bool                 # any page has tables or multi-column layout

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pdf-inspector 1.14.2

pdf-inspector

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