Files
pdf-inspector/napi
Abimael MartellandClaude Opus 4.7 73cffed1da detect_tables: catch narrow-column undercount via text-cluster topology (#93)
text_cluster_column_undercount previously fired only on tables with
6+ markdown columns. That missed a common production failure shape:
4-column page geometry where the heuristic detector's x-position
clustering collapses 2 narrow numeric columns (dates, IDs, amounts)
into adjacent wider columns, producing 2-column markdown.

The original 6-column floor existed because raw x-cluster count is
noisy on small tables — wrapped continuations, bullet indents, and
within-cell text variation produce many small x-clusters that don't
correspond to real columns. Examples:

  - Pcmso-style 2-column key/value layout with multi-line values
    shows 10 raw x-clusters but only 1 has more than a single item.
  - Yale-style archival catalog with 3 columns shows 6 raw
    x-clusters but most clusters are single-item continuations.

Replace the raw cluster count with a "significant cluster" count:
clusters whose item count is at least 1/4 of the dominant cluster
(and ≥2 items). That filters the within-cell-variation noise while
preserving signal from real columns, which consistently have one
item per row.

With significant-cluster counting:
  - Narrow-undercount fires when significant_clusters >= 3 AND
    significant_clusters >= table_cols * 2 (catches 4-col-collapsed-
    to-2 cases without misfiring on pcmso 10-clusters or yale 6
    where the significant subset matches markdown).
  - Legacy wide-undercount path (table_cols >= 6) keeps the same
    +2 / 1.2x thresholds, now applied to significant clusters
    instead of raw clusters.

Verified against representative regression and must-not-regress
cases from prior PR cycles: narrow-undercount catches genuine
column-drop cases (significant=4, markdown=2 → routes to OCR);
must-not-regress fixtures (italian-gov 6520 chars, pcmso 1733
chars, yale 1754 chars, doc200/182/189 still routing to OCR via
PR #91 guards) unchanged.

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-18 17:26:35 -07:00
..

PDF Inspector

Fast PDF classification and region-based text extraction for Node.js/Bun. Native Rust performance via napi-rs.

Built by Firecrawl for hybrid OCR pipelines — extract text from PDF structure where possible, fall back to OCR only when needed.

Install

npm install @firecrawl/pdf-inspector
# or
bun add @firecrawl/pdf-inspector

Prebuilt binaries included for linux-x64 and macOS ARM64. No Rust toolchain needed.

API

classifyPdf(buffer: Buffer): PdfClassification

Classify a PDF as TextBased, Scanned, Mixed, or ImageBased (~10-50ms). Returns which pages need OCR.

import { classifyPdf } from '@firecrawl/pdf-inspector'
import { readFileSync } from 'fs'

const pdf = readFileSync('document.pdf')
const result = classifyPdf(pdf)

console.log(result.pdfType)        // "TextBased" | "Scanned" | "Mixed" | "ImageBased"
console.log(result.pageCount)      // 42
console.log(result.pagesNeedingOcr) // [5, 12, 15] (0-indexed)
console.log(result.confidence)     // 0.875

extractTextInRegions(buffer: Buffer, pageRegions: PageRegions[]): PageRegionTexts[]

Extract text within bounding-box regions from a PDF. Designed for hybrid OCR pipelines where a layout model detects regions in rendered page images, and this function extracts text from the PDF structure for text-based pages — skipping GPU OCR.

Each region result includes a needsOcr flag that signals unreliable extraction (empty text, GID-encoded fonts, garbage text, encoding issues).

import { extractTextInRegions } from '@firecrawl/pdf-inspector'

const result = extractTextInRegions(pdf, [
  {
    page: 0, // 0-indexed
    regions: [
      [0, 0, 300, 400],    // [x1, y1, x2, y2] in PDF points, top-left origin
      [300, 0, 612, 400],
    ]
  }
])

for (const region of result[0].regions) {
  if (region.needsOcr) {
    // Unreliable text — send this region to OCR instead
  } else {
    console.log(region.text) // Extracted text in reading order
  }
}

Types

interface PdfClassification {
  pdfType: string          // "TextBased" | "Scanned" | "Mixed" | "ImageBased"
  pageCount: number
  pagesNeedingOcr: number[] // 0-indexed page numbers
  confidence: number        // 0.0 - 1.0
}

interface PageRegions {
  page: number              // 0-indexed
  regions: number[][]       // [[x1, y1, x2, y2], ...] in PDF points, top-left origin
}

interface PageRegionTexts {
  page: number
  regions: RegionText[]
}

interface RegionText {
  text: string
  needsOcr: boolean         // true when text is unreliable
}

Platforms

Platform Architecture Supported
Linux x64 Yes
macOS ARM64 Yes

License

MIT