* fix: reduce false OCR recommendations for text PDFs with figure images
Two fixes in the detector:
1. Fix Tf operator parsing: some PDFs concatenate Tf directly with the
next operator (e.g. "25 Tf[<01>...") without whitespace. The scanner
now accepts [, (, <, / as valid followers, fixing font_changes being
reported as 0.
2. Distinguish text-with-figures from scanned-with-OCR: pages with
multiple images (image_count > 1) and strong text signals (text_ops
>= 50, alphanum >= 10) are recognized as text pages with figures,
not scanned templates. Scanned PDFs have exactly 1 full-page image.
This prevents academic papers, reports with charts, and similar PDFs
from being incorrectly classified as Mixed/OCR-needed when their text
is perfectly extractable.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* chore: bump napi version to 0.7.2
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: remove template image influence from page classification
Template images (large background/figure images) no longer affect
pages_needing_ocr. In the region-based pipeline, text regions are
extracted independently from image regions, and per-region needs_ocr
quality checks handle scanned-with-OCR garbage text.
Also makes the invisible text retry (for OCR text layers) trigger on
text quality rather than PDF type, so it works regardless of
classification.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Revert "fix: remove template image influence from page classification"
This reverts commit 100cbe5453.
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
firecrawl-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