Tanod Docs PDF OCR

x402

Tanod Docs PDF OCR is a paid API for AI agents from docs.tanod.dev, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-13).

Performs OCR on a PDF to make it text-searchable, returning a searchable PDF with extracted text

Price / call$0.01

Seller volume →—

Success—

StatusUnknown

How this service works

Documents: DOCX template fill, Office to PDF, any file to Markdown, PDF to Word, OCR, PDF pipeline.

Output

Returns a JSON object containing the searchable PDF (either as a base64-encoded file or a 24-hour download URL), the extracted text string, list of pages that were OCR'd, list of pages skipped because they already had text, DPI and language used, input/output byte sizes, and a flag indicating if any active content was removed for security.

Example use cases

Digitize scanned contract for search

“I have a scanned contract at https://example.com/contract.pdf — can you run OCR on pages 1-5 at 200 DPI and give me a download link to the searchable version so I can search through the text?”

Extract invoice text for data entry

“Please OCR this invoice PDF — I'll give it to you as a base64 string — just pages 1 and 2 at 150 DPI in English, and return the result inline as base64 so I can grab the extracted text.”

Make research paper archive searchable

“I have a bunch of scanned research papers and need to make them text-searchable. Start with this one: https://example.com/paper.pdf, OCR all pages up to 10 at 200 DPI, skip any pages that already have text, and give me back a download URL.”

Technical details

Endpoint

POSThttps://docs.tanod.dev/v1/pdf/ocr?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need to make a scanned or image-based PDF text-searchable using OCR, especially when you want to preserve the original PDF format while adding an invisible text layer. Prefer this over general document conversion endpoints when the specific goal is optical character recognition on PDFs, particularly for invoices, contracts, or scanned archives. Use the url output mode when the resulting file would be large and you want to avoid bloating the agent context window with base64 data. The skip_text option makes it efficient for mixed documents that are partly already searchable.

Side effects

PDF URL exceeds 20 MB size limit — request rejected
URL points to a private, internal, or IP-literal host — refused
More than 10 pages requested in the page range — validation error
DPI value outside 100-300 range — validation error
More than 5 pages submitted with DPI above 200 — may be rejected or throttled
Unsupported Tesseract language code provided — error returned
Both url and pdf_base64 omitted — missing input error
PDF base64 exceeds 10 MB decoded — request rejected

Input

Schema · Request body

Field	Type	Description
dpi	integer	100-300 (default 200); at most 200 for more than 5 pages.
url	—	Public http(s) URL of the PDF (at most 20 MB; private, internal and IP-literal hosts are refused); or use pdf_base64.
lang	string	Installed tesseract languages only, '+'-joined (eng today).
pages	string	Pages to OCR, at most 10, each part N, N-M, -M or last (no open N- ranges), e.g. 1-3,7. At most 5 pages is the lower price.
output	string	base64 (default): the file comes back inline as base64. url: the file is stored for 24 hours and the reply carries a download link (url, expires_at, bytes, content_type, sha256) instead of the base64, which keeps the file out of your context window.
skip_text	boolean	Leave pages that already have text alone.
pdf_base64	—	The PDF as base64 (at most 10 MB decoded per request); or use url.

Output

Schema · Response

{
  "type": "json",
  "example": {
    "dpi": 200,
    "file": {
      "name": "searchable.pdf",
      "bytes": 15472,
      "pages": 1,
      "data_base64": "JVBERi0xLjQKJb/3... (trimmed: 20,632 base64 characters in the reply)",
      "content_type": "application/pdf"
    },
    "lang": "eng",
    "text": "Dummy PDF file",
    "ocr_pages": [
      1
    ],
    "operation": "ocr",
    "input_bytes": 13264,
    "input_pages": 1,
    "output_bytes": 15472,
    "text_truncated": false,
    "skipped_has_text": [],
    "untrusted_content": true,
    "active_content_removed": {}
  }
}

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