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Compute Revenue Lab — PDF OCR Page Map
x402
Compute Revenue Lab — PDF OCR Page Map is a paid API for AI agents from x402.bakdupoffroad.com, paid per call via x402, $0.018/call, status down (last checked 2026-09-15).
Analyzes a base64-encoded PDF and returns a per-page classification map indicating which pages need OCR, have selectable text, or are image-only, along with a summary of OCR cost estimates.
Price / call$0.018
Success—
StatusDown
How this service works
Private PDF preflight and authorized-video intelligence APIs paid per call with x402 USDC on Base.
Output
Returns a JSON object with a per-page array (page number, classification as 'selectable' or 'image_or_blank', OCR necessity flag, image count, and character count), plus a summary (selectable page count, estimated OCR pages, avoided full-OCR pages, sparse pages), total page count, a SHA-256 hash of the source PDF, schema version, and pricing economics (net cost in micro-USD).
Example use cases
Pre-OCR cost estimation for legal documents
“I have a 40-page legal contract as a PDF — can you scan through it and tell me which pages are already text-selectable and which ones are scanned images so I can figure out how many pages will actually need OCR before I run it?”
Automated PDF ingestion pipeline triage
“Before I process this batch of uploaded PDFs, can you profile this document and give me a summary of how many pages need OCR versus how many already have extractable text, so I can route it to the right processing queue?”
Quality check on digitized archive scans
“I just received a digitized archive PDF — can you classify each page and tell me which ones are blank or image-only and which ones already have selectable text? I want the full page-by-page breakdown including character counts.”
Technical details
Endpoint
POSThttps://x402.bakdupoffroad.com/v1/document/ocr-page-mapWhen to prefer
Choose this endpoint when you need a lightweight, privacy-preserving preflight pass over a PDF before committing to full OCR processing — it tells you exactly which pages need OCR and which already have selectable text, saving downstream OCR cost. Prefer it over running full OCR blindly when you want to triage documents, estimate costs, or route pages to different processing pipelines. Well-suited for pipelines that must pay per-call via USDC on Base using the x402 protocol.
Side effects
- PDF exceeds the service's decoded-size limit — request rejected
- Malformed or non-PDF base64 payload — parsing error returned
- Payment failure via x402 USDC on Base — 402 response, no analysis performed
- Network timeout for very large PDFs during processing
- Invalid base64 encoding — decoding error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| pdfBase64 | string | A base64-encoded PDF, up to the service's advertised decoded-size limit. Customer text is never returned or retained. |
Output
Schema · Response
{
"type": "json",
"example": {
"jobId": "00000000-0000-4000-8000-000000000000",
"result": {
"pages": [
{
"needsOcr": false,
"imageCount": 0,
"pageNumber": 1,
"classification": "selectable",
"textCharacters": 412
},
{
"needsOcr": true,
"imageCount": 1,
"pageNumber": 2,
"classification": "image_or_blank",
"textCharacters": 0
}
],
"summary": {
"sparsePages": 0,
"selectablePages": 3,
"estimatedOcrPages": 1,
"avoidedFullOcrPages": 3
},
"pageCount": 4,
"sourceSha256": "0000000000000000000000000000000000000000000000000000000000000000",
"schemaVersion": "1",
"profileTruncated": false,
"profiledPageCount": 4
},
"economics": {
"netMicroUsd": 18000,
"energyMicroUsd": 0,
"revenueMicroUsd": 18000,
"platformFeeMicroUsd": 0
}
}
}Tags
x402
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