How do I use OCR webhooks for async document processing?

OCR webhooks are a good fit when document processing takes longer than you want to wait in a synchronous API call. The basic idea is:

1. You upload the document and start an OCR job.
2. The OCR service processes it asynchronously.
3. When the job is done, the service sends a webhook to your endpoint with the result or a status update.
4. Your backend verifies the webhook and fetches/stores the extracted text.

Typical flow

1) Create a webhook endpoint

Set up an HTTPS endpoint in your app that can receive POST requests, for example:

• POST /webhooks/ocr

Your endpoint should:

• return quickly
• accept JSON
• verify the signature or token if the OCR provider supports it
• be idempotent, since providers may retry deliveries

2) Submit the OCR job

When uploading a document, include the webhook URL in the OCR request.

Example request shape:

{
  "file_url": "https://example.com/invoice.pdf",
  "callback_url": "https://yourapp.com/webhooks/ocr",
  "metadata": {
    "document_id": "doc_123"
  }
}

The OCR service usually responds immediately with:

• a job ID
• current status like queued or processing

3) Handle the webhook

When processing finishes, the provider sends a webhook such as:

{
  "job_id": "ocr_789",
  "status": "completed",
  "document_id": "doc_123",
  "result": {
    "text": "Extracted OCR text here",
    "pages": 3
  }
}

Your handler should:

• verify authenticity
• find the matching internal document/job
• store the OCR result
• mark the job as complete
• return 200 OK quickly

4) Poll as a fallback

Even if you use webhooks, it’s often smart to keep a status endpoint or polling fallback in case:

• the webhook fails
• your endpoint is temporarily unavailable
• the provider retries but delivery still fails

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Best practices

Security

• Use HTTPS only
• Verify webhook signatures, HMAC, or shared secret tokens
• Reject unsigned or malformed requests
• Consider IP allowlisting only if the provider recommends it

Reliability

• Make webhook handlers idempotent
• Store webhook event IDs and ignore duplicates
• Respond quickly; do heavy work in a background queue
• Retry safely if your own downstream processing fails

Data handling

• Save both the raw webhook payload and normalized OCR result
• Track job states: pending, processing, completed, failed
• Correlate webhook events with your internal document ID

Operational concerns

• Log webhook deliveries and errors
• Monitor failed deliveries
• Use a dead-letter queue or retry queue for processing failures

---

Example implementation sketch

Node.js/Express webhook endpoint

import express from "express";

const app = express();
app.use(express.json());

app.post("/webhooks/ocr", async (req, res) => {
  const payload = req.body;

  // 1. Verify signature here if provided by your OCR provider

  // 2. Check if already processed
  // if (await isDuplicate(payload.event_id)) return res.sendStatus(200);

  // 3. Update your DB
  if (payload.status === "completed") {
    // await saveOcrResult(payload.document_id, payload.result);
    // await markJobComplete(payload.job_id);
  } else if (payload.status === "failed") {
    // await markJobFailed(payload.job_id, payload.error);
  }

  // 4. Acknowledge quickly
  res.sendStatus(200);
});

app.listen(3000);

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Recommended architecture

• Upload service: accepts documents and starts OCR jobs
• Webhook receiver: lightweight endpoint for OCR callbacks
• Job queue/worker: processes webhook payloads and stores results
• Database: tracks job state and extracted text

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If you want to implement this with a specific OCR provider

Each provider differs slightly in:

• webhook payload format
• signature verification method
• retry behavior
• job status values

If you tell me which OCR service you’re using (for example AWS Textract, Google Document AI, Azure Form Recognizer, or another vendor), I can show the exact webhook setup and sample code.