Extract text from any image— Latin, CJK, Cyrillic, mixed scripts.

import os, requests
from pathlib import Path

resp = requests.post(
    "https://api.msightflow.ai/v1/ocr",
    headers={"Authorization": f"Bearer {os.environ['MSF_API_KEY']}"},
    files={"image": Path("receipt.jpg").read_bytes()},
    data={"language": "en", "provider": "easyocr"},
)
result = resp.json()
print(result["full_text"])
for r in result["regions"]:
    print(f"  {r['confidence']:.2f}  {r['box']}  {r['text']}")

# Hard layout? Fall back to GPT-4o for structured output.
resp = requests.post(
    "https://api.msightflow.ai/v1/ocr",
    headers={"Authorization": f"Bearer {os.environ['MSF_API_KEY']}"},
    files={"image": Path("invoice.jpg").read_bytes()},
    data={
        "provider": "cloud",
        "model": "gpt-4o",
        "output_schema": '{"invoice_number": "string", "total": "number", "date": "string"}',
    },
)
print(resp.json()["structured"])
# → {"invoice_number": "INV-00231", "total": 423.50, "date": "2026-04-12"}

import fetch from "node-fetch";
import FormData from "form-data";
import fs from "fs";

const form = new FormData();
form.append("image", fs.createReadStream("receipt.jpg"));
form.append("language", "en");

const resp = await fetch("https://api.msightflow.ai/v1/ocr", {
  method: "POST",
  headers: { Authorization: `Bearer ${process.env.MSF_API_KEY}` },
  body: form,
});
const { full_text } = await resp.json();
console.log(full_text);

curl -X POST https://api.msightflow.ai/v1/ocr \
  -H "Authorization: Bearer $MSF_API_KEY" \
  -F "image=@receipt.jpg" \
  -F "language=en"