Files
pdf-inspector/README.md
T
2026-02-18 10:34:32 -08:00

276 lines
11 KiB
Markdown

# pdf-inspector
Fast Rust library for 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.
Built by [Firecrawl](https://firecrawl.dev) 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** — Detect TextBased, Scanned, ImageBased, or Mixed PDFs in ~10-50ms by sampling content streams. Returns a confidence score (0.0-1.0) and per-page OCR routing.
- **Text extraction** — Position-aware extraction with font info, X/Y coordinates, and automatic multi-column reading order.
- **Markdown conversion** — Headings (H1-H4 via font size ratios), bullet/numbered/letter lists, code blocks (monospace font detection), tables (rectangle-based and heuristic), bold/italic formatting, URL linking, and page breaks.
- **Table detection** — Dual-mode: rectangle-based detection from PDF drawing ops, plus heuristic detection from text alignment. Handles financial tables, footnotes, and continuation tables across pages.
- **CID font support** — ToUnicode CMap decoding for Type0/Identity-H fonts, UTF-16BE, UTF-8, and Latin-1 encodings.
- **Multi-column layout** — Automatic detection of newspaper-style columns, sequential reading order, and RTL text support.
- **Lightweight** — Pure Rust, no ML models, no external services. Single dependency on `lopdf` for PDF parsing.
## Quick start
### As a library
Add to your `Cargo.toml`:
```toml
[dependencies]
pdf-inspector = { git = "https://github.com/firecrawl/pdf-inspector" }
```
Detect and extract in one call:
```rust
use pdf_inspector::process_pdf;
let result = process_pdf("document.pdf")?;
println!("Type: {:?}", result.pdf_type); // TextBased, Scanned, ImageBased, Mixed
println!("Confidence: {:.0}%", result.confidence * 100.0);
println!("Pages: {}", result.page_count);
if let Some(markdown) = &result.markdown {
println!("{}", markdown);
}
```
Or detect without extracting:
```rust
use pdf_inspector::detect_pdf_type;
let detection = detect_pdf_type("document.pdf")?;
match detection.pdf_type {
pdf_inspector::PdfType::TextBased => {
// Extract locally — fast and free
}
_ => {
// Route to OCR service
// detection.pages_needing_ocr tells you exactly which pages
}
}
```
Customize the detection scan strategy:
```rust
use pdf_inspector::{process_pdf_with_config, DetectionConfig, ScanStrategy};
// Scan all pages for accurate Mixed vs Scanned classification
let config = DetectionConfig {
strategy: ScanStrategy::Full,
..Default::default()
};
let result = process_pdf_with_config("document.pdf", config)?;
// Sample 5 evenly distributed pages (fast for large PDFs)
let config = DetectionConfig {
strategy: ScanStrategy::Sample(5),
..Default::default()
};
let result = process_pdf_with_config("large.pdf", config)?;
// Only check specific pages
let config = DetectionConfig {
strategy: ScanStrategy::Pages(vec![1, 5, 10]),
..Default::default()
};
let result = process_pdf_with_config("known-layout.pdf", config)?;
```
Process from a byte buffer (no filesystem needed):
```rust
use pdf_inspector::process_pdf_mem;
let bytes = std::fs::read("document.pdf")?;
let result = process_pdf_mem(&bytes)?;
```
### CLI
```bash
# Convert PDF to Markdown
cargo run --bin pdf2md -- document.pdf
# JSON output (for piping)
cargo run --bin pdf2md -- document.pdf --json
# Detection only (no extraction)
cargo run --bin detect-pdf -- document.pdf
cargo run --bin detect-pdf -- document.pdf --json
```
## Architecture
```
PDF bytes
├─► detector → PdfType (TextBased / Scanned / ImageBased / Mixed)
└─► extractor
├─ fonts → font widths, encodings
├─ content_stream → walk PDF operators → TextItems + PdfRects
├─ xobjects → Form XObject text, image placeholders
├─ links → hyperlinks, AcroForm fields
└─ layout → column detection → line grouping → reading order
├─► tables
│ ├─ detect_rects → rectangle-based tables (union-find)
│ ├─ detect_heuristic → alignment-based tables
│ ├─ grid → column/row assignment → cells
│ └─ format → cells → Markdown table
└─► markdown
├─ analysis → font stats, heading tiers
├─ preprocess → merge headings, drop caps
├─ convert → line loop + table/image insertion
├─ classify → captions, lists, code
└─ postprocess → cleanup → final Markdown
```
### Project structure
```
src/
lib.rs — Public API, re-exports
types.rs — Shared types: TextItem, TextLine, PdfRect, ItemType
text_utils.rs — Character/text helpers (CJK, RTL, ligatures, bold/italic)
detector.rs — Fast PDF type detection without full document load
glyph_names.rs — Adobe Glyph List → Unicode mapping
tounicode.rs — ToUnicode CMap parsing for CID-encoded text
extractor/ — Text extraction pipeline
tables/ — Table detection and formatting
markdown/ — Markdown conversion and structure detection
bin/ — CLI tools (pdf2md, detect_pdf)
```
## How classification works
1. Parse the xref table and page tree (no full object load)
2. Select pages based on `ScanStrategy` (default: all pages with early exit)
3. Look for `Tj`/`TJ` (text operators) and `Do` (image operators) in content streams
4. Classify based on text operator presence across sampled pages
This detects 300+ page PDFs in milliseconds. The result includes `pages_needing_ocr` — a list of specific page numbers that lack text, enabling per-page OCR routing instead of all-or-nothing.
### Scan strategies
| Strategy | Behavior | Best for |
|---|---|---|
| `EarlyExit` (default) | Scan all pages, stop on first non-text page | Pipelines routing TextBased PDFs to fast extraction |
| `Full` | Scan all pages, no early exit | Accurate Mixed vs Scanned classification |
| `Sample(n)` | Sample `n` evenly distributed pages (first, last, middle) | Very large PDFs where speed matters more than precision |
| `Pages(vec)` | Only scan specific 1-indexed page numbers | When the caller knows which pages to check |
## API
### Functions
| Function | Description |
|---|---|
| `process_pdf(path)` | Detect, extract, and convert to Markdown |
| `process_pdf_with_config(path, config)` | Same, with custom `DetectionConfig` |
| `process_pdf_mem(bytes)` | Same, from a byte buffer |
| `process_pdf_mem_with_config(bytes, config)` | Same, from bytes with custom config |
| `detect_pdf_type(path)` | Classification only (fastest) |
| `detect_pdf_type_with_config(path, config)` | Classification with custom config |
| `detect_pdf_type_mem(bytes)` | Classification from bytes |
| `detect_pdf_type_mem_with_config(bytes, config)` | Classification from bytes with custom config |
| `extract_text(path)` | Plain text extraction |
| `extract_text_with_positions(path)` | Text with X/Y coordinates and font info |
| `to_markdown(text, options)` | Convert plain text to Markdown |
| `to_markdown_from_items(items, options)` | Markdown from pre-extracted `TextItem`s |
| `to_markdown_from_items_with_rects(items, options, rects)` | Markdown with rectangle-based table detection |
### Types
| Type | Description |
|---|---|
| `PdfType` | `TextBased`, `Scanned`, `ImageBased`, `Mixed` |
| `PdfProcessResult` | Full result: markdown, metadata, confidence, timing |
| `PdfTypeResult` | Detection result: type, confidence, page count, pages needing OCR |
| `DetectionConfig` | Configuration for detection: scan strategy, thresholds |
| `ScanStrategy` | `EarlyExit`, `Full`, `Sample(n)`, `Pages(vec)` |
| `TextItem` | Text with position, font info, and page number |
| `MarkdownOptions` | Configuration for Markdown conversion |
| `PdfError` | `Io`, `Parse`, `Encrypted`, `InvalidStructure`, `NotAPdf` |
## Markdown output
The converter handles:
| Element | How it's detected |
|---|---|
| Headings (H1-H4) | Font size tiers relative to body text, with 0.5pt clustering |
| Bold/italic | Font name patterns (Bold, Italic, Oblique) |
| Bullet lists | `*`, `-`, `*`, `○`, `●`, `◦` prefixes |
| Numbered lists | `1.`, `1)`, `(1)` patterns |
| Letter lists | `a.`, `a)`, `(a)` patterns |
| Code blocks | Monospace fonts (Courier, Consolas, Monaco, Menlo, Fira Code, JetBrains Mono) and keyword detection |
| Tables | Rectangle-based detection from PDF drawing ops + heuristic detection from text alignment |
| Financial tables | Token splitting for consolidated numeric values |
| Captions | "Figure", "Table", "Source:" prefix detection |
| Sub/superscript | Font size and Y-offset relative to baseline |
| URLs | Converted to Markdown links |
| Hyphenation | Rejoins words broken across lines |
| Page numbers | Filtered from output |
| Drop caps | Large initial letters merged with following text |
| Dot leaders | TOC-style dots collapsed to " ... " |
## Debugging with RUST_LOG
Structured logging via `RUST_LOG` replaces the former debug binaries. Set the environment variable to control which sections emit debug output on stderr:
```bash
# Raw PDF content stream operators (replaces dump_ops)
RUST_LOG=pdf_inspector::extractor::content_stream=trace cargo run --bin pdf2md -- file.pdf > /dev/null
# Font metadata, encodings, ligatures (replaces debug_fonts / debug_ligatures)
RUST_LOG=pdf_inspector::extractor::fonts=debug cargo run --bin pdf2md -- file.pdf > /dev/null
# ToUnicode CMap parsing
RUST_LOG=pdf_inspector::tounicode=debug cargo run --bin pdf2md -- file.pdf > /dev/null
# Text items per page with x/y/width (replaces debug_spaces / debug_pages)
RUST_LOG=pdf_inspector::extractor=debug cargo run --bin pdf2md -- file.pdf > /dev/null
# Column detection and reading order (replaces debug_order)
RUST_LOG=pdf_inspector::extractor::layout=debug cargo run --bin pdf2md -- file.pdf > /dev/null
# Y-gap analysis and paragraph thresholds (replaces debug_ygaps)
RUST_LOG=pdf_inspector::markdown::analysis=debug cargo run --bin pdf2md -- file.pdf > /dev/null
# Table detection
RUST_LOG=pdf_inspector::tables=debug cargo run --bin pdf2md -- file.pdf > /dev/null
# Everything
RUST_LOG=pdf_inspector=debug cargo run --bin pdf2md -- file.pdf > /dev/null
```
## Use case: smart PDF routing
pdf-inspector was built for pipelines that process PDFs at scale. Instead of sending every PDF through OCR:
```
PDF arrives
→ pdf-inspector classifies it (~20ms)
→ TextBased + high confidence?
YES → extract locally (~150ms), done
NO → send to OCR service (2-10s)
```
This saves cost and latency for the majority of PDFs that are already text-based (reports, papers, invoices, legal docs).
## License
MIT