v0 Tutorial5 min setupLow-code friendly

Add Invoice OCR to Your v0 App in 5 Minutes

v0 scaffolds your React UI. StructOCR reads the invoices. Here's the exact prompt and API call to wire them together — zero backend required.

Diagram showing an invoice image uploaded in a v0 React app, sent to the StructOCR API, and returned as structured JSON with vendor name, total, and line items.
Upload → parse → display: the full flow takes a single API call.

Why v0 + StructOCR is a natural fit

v0 generates beautiful, accessible React interfaces using Tailwind CSS and shadcn/ui directly from plain English. It handles the frontend components perfectly — but document parsing is still something you have to wire up yourself. StructOCR fills exactly that gap: one API call, structured JSON back. Tell v0 what you want, paste in the fetch code below, and you're done.

The problem: AI builders don't read documents

Tools like v0 are remarkable at generating UIs instantly — but they stop at the file boundary. You can build a beautiful invoice upload interface in minutes, yet the actual extraction of vendor names, line items, totals, and dates still requires a dedicated Invoice OCR API. Most teams either skip this feature or spend days integrating a general-purpose vision model that returns unstructured text.

The solution: one API call, structured JSON

StructOCR's /v1/invoice endpoint accepts an image and returns a clean JSON object with every field pre-labeled — no prompting, no parsing, no post-processing. Because it speaks plain HTTP, v0 can wire it up from a single fetch call.

Integrate StructOCR in v0 (3 Steps)

  1. Create a v0 project and describe your app

    Open v0.dev and start a new prompt. Describe the app you want to build — be specific about the invoice fields you need.

    Try this prompt: "Build an invoice upload UI using shadcn. Users can upload an invoice image and see the vendor name, invoice date, due date, and total amount extracted automatically in a clean card."
  2. Tell v0 to call the StructOCR API

    Once v0 scaffolds the UI, paste the follow-up prompt below. v0 will update the React code to handle the file upload and wire the API response to your UI components.

    Follow-up prompt: "When a user uploads an image, send it as a multipart/form-data POST to https://api.structocr.com/v1/invoice with an Authorization header containing my API key. Display the returned JSON fields in the results panel."
    // v0 generates something like this in your React component const response = await fetch('https://api.structocr.com/v1/invoice', { method: 'POST', headers: { Authorization: `Bearer ${process.env.NEXT_PUBLIC_STRUCTOCR_KEY}` }, body: formData, });
  3. Add your StructOCR API key

    When you export your v0 project to a Next.js codebase or deploy to Vercel, simply add your StructOCR key to your .env.local file or Vercel Environment Variables.

    Variable name: NEXT_PUBLIC_STRUCTOCR_KEY — get your free key at structocr.com/dashboard
    NEXT_PUBLIC_STRUCTOCR_KEY=sk_live_xxxxxxxxxxxxxxxx

What you can build in v0 with this integration

  • Accounts payable dashboard: Let your team upload supplier invoices and automatically populate a tracker with vendor, amount, and due date — no manual data entry. Perfect for building custom accounts payable automation workflows.
  • Freelance invoice manager: Build a personal tool that reads your client invoices, calculates outstanding totals, and exports to CSV — all inside v0 in under an hour.
  • Expense approval workflow: Add an upload step to an approval flow: extract invoice details automatically and route for sign-off without any copy-pasting. This is the foundation of modern expense management automation.
  • Multi-vendor price comparison: Parse invoices from multiple suppliers and compare line-item pricing side by side — a task that previously required a developer and a parsing library.

The prompt and the code

Copy the follow-up prompt into your v0 chat to generate the integration. The code block shows what v0 typically produces — useful if you want to verify or customize it.

Prerequisite: A free StructOCR account — get your API key at structocr.com/dashboard (no credit card required)

I need to add invoice OCR to this app.

When a user uploads an image:
1. Send it as multipart/form-data to: POST https://api.structocr.com/v1/invoice
2. Include the header: Authorization: Bearer ${process.env.NEXT_PUBLIC_STRUCTOCR_KEY}
3. The response is JSON with fields: vendor_name, invoice_date, due_date, total_amount, currency, line_items[]
4. Display each field in the results panel below the upload button
5. Show a loading spinner while the request is in flight
6. Show an error message if the upload fails

Use the environment variable NEXT_PUBLIC_STRUCTOCR_KEY for the API key.

What the API returns

Every field is labeled and typed. Your v0 React components can bind this directly to the UI without any transformation.

{
  "invoice_number": "INV-2024-0891",
  "vendor_name": "Acme Supplies Ltd.",
  "vendor_address": "123 Commerce St, Austin TX 78701",
  "invoice_date": "2024-11-14",
  "due_date": "2024-12-14",
  "currency": "USD",
  "subtotal": 2400,
  "tax": 192,
  "total_amount": 2592,
  "line_items": [
    {
      "description": "Widget Pro (12-pack)",
      "quantity": 4,
      "unit_price": 500,
      "amount": 2000
    },
    {
      "description": "Shipping & Handling",
      "quantity": 1,
      "unit_price": 400,
      "amount": 400
    }
  ],
  "confidence": 0.97
}

Technical Specs

  • Formats accepted: PNG, JPG, WEBP — including scanned and photographed invoices
  • Response time: typically 1–3 seconds per page
  • Languages: English, Spanish, French, German, Chinese, Japanese, and 40+ more
  • Max file size: 20 MB per request
  • Confidence scores: each field includes an extraction confidence value

Key Features

  • Structured output: every field is named and typed — no regex, no parsing
  • Line items: products, quantities, unit prices, and subtotals extracted as an array
  • Tax and discount: VAT, GST, and discount rows are detected and labeled separately
  • Multi-page support: invoices spanning multiple pages are handled in a single call
  • Works with photos: blurry or skewed smartphone photos are corrected automatically

Live Demo: Invoice extractor

No registration required. Upload a file to test the extraction.

1
Upload
2
Results

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Frequently Asked Questions

Does StructOCR work inside a v0 app?

Yes. StructOCR is a standard REST API that accepts a file and returns JSON — v0 can easily generate the client-side `fetch` logic to call it.

Where should I store the StructOCR API key in v0?

While you can prototype using client-side `NEXT_PUBLIC_` variables in Next.js, for production applications we strongly recommend moving the API call to a Next.js Server Action or API Route. This keeps your API key completely hidden from the browser.

Can v0 call StructOCR without any backend?

Yes, for prototyping. The StructOCR API supports CORS, so a browser fetch works out of the box. However, when deploying a v0 app to Vercel for production, it's best practice to use Next.js server-side routing.

What invoice formats does StructOCR support?

PNG, JPG, and WEBP. Files up to 20 MB are accepted.

How accurate is the invoice extraction?

Accuracy on clean invoices image typically exceeds 97%. Scanned or photographed invoices depend on image quality, but the API includes a confidence score per field. You can verify this live with your own documents using our free invoice extractor tool.

Is there a free tier to test with?

Yes. StructOCR's free plan includes 10 pages per month with no credit card required. Check out our pricing page for full details or sign up at structocr.com/dashboard.

Can I use this with Lovable, Bolt.new, or Replit instead of v0?

Absolutely — the fetch code is identical across all AI builders. The only difference is where you add the environment variable: VITE_ variables for Lovable or Bolt.new, and Replit Secrets for Replit.

What if my invoice is not in English?

StructOCR supports 40+ languages including Spanish, French, German, Chinese, Japanese, and Arabic. The API auto-detects the document language.

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