Extract text and structure from images

Superlinked gives your agent one API to turn PDFs, screenshots, charts and photos into structured data.

Structured output

invoice_no   123
customer     Acme Corp.
line_items   3
total        $385.00

Faded thermal receipt from the CORD dataset — `item Kupon 15 subtotal 100,909 tax 10,091 total 111,000`

CORD test image 0007 · CC BY 4.0

NVIDIA annual report page with financial tables and footnotes — `location 2026 2025 2024 United States 149,617 77,482 31,533 Taiwan 42,345 23,600 14,912 China 19,677 25,048 12,330`

NVIDIA 2026 Annual Report, page 78

Bubble chart of employment change and weekly earnings by industry — `industry jobs weekly [1] Private education and health services +69k $1,184 [2] Professional and business services +36k $1,674 [3] Leisure and hospitality -61k $602 [4] Information -9k $2,071`

U.S. Bureau of Labor Statistics · June 2026

Weathered shipping container with an ISO identifier — `container_id CCLU 714026 4 iso_type 45G1`

Col André Kritzinger · CC BY-SA 3.0

Product label with GTIN, best-before date, and batch number — `gtin 27394376616222 best_before 2024-02-12 batch 3043AAG`

Mike1024 · public domain

Worn factory control panel with operational labels — `button E-STOP RESET switch CONVEYORS ON/OFF dial FILLER SPEED control UP CLIPPER DOWN`

darkday · CC BY 2.0

Run OCR with one extract call

from pathlib import Path
from sie_sdk import SIEClient
client = SIEClient(
    api_key="API keysk-sie-…",
    base_url="https://api.superlinked.com",
)
image = {"data": Path(imagestore-receipt.jpgbrowse).read_bytes(), "format": "jpeg"}
result = client.extract(
    "modellightonai/LightOnOCR-2-1B",
    {"images": [image]},
)
print(result["entities"][0]["text"])

import { readFile } from 'node:fs/promises';
import { SIEClient } from '@superlinked/sie-sdk';

const client = new SIEClient('https://api.superlinked.com', {
  apiKey: 'sk-sie-…',
});

const image = await readFile("store-receipt.jpg");
const result = await client.extract(
  'lightonai/LightOnOCR-2-1B',
  { images: [image] },
  { labels: [] },
);
console.log(result.entities[0].text);

images_bytes=$(base64 < 'store-receipt.jpg' | tr -d '\n')
curl https://api.superlinked.com/v1/extract/lightonai%2FLightOnOCR-2-1B \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"items\":[{\"images\":[{\"data\":\"$images_bytes\",\"format\":\"jpeg\"}]}]}"

Build the "OCR" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: lightonai/LightOnOCR-2-1B (SIE primitive: /extract). Keep the model id configurable.

Task
- Input: an uploaded image containing text.
- Behaviour: return the text visible in the image
- Call the selected SIE primitive once per request and map the response into your domain type.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.

Run

Output

Source store-receipt.jpg — source · store-receipt.jpg

MARKET 42

Oat milk 3.20

Rye bread 2.80

Coffee beans 9.40

Total 15.40

Reads text directly from images and photos

PRICE

$ / 1k pages ↓

$1.5 AWS Textract

$1.5 Google Enterprise Document OCR

$1.5 Azure Document Intelligence Read

$1.86 SIE docling OCR

SIE LightOnOCR 2 1B $2

Mistral OCR 4 $4

QUALITY

Accuracy · olmOCR ↑

SIE LightOnOCR 2 1B 0.78

0.75

0.74

0.33 SIE docling OCR

0.32

0.31

LATENCY

p50 ms ↓

320ms SIE docling OCR

360ms

390ms

SIE LightOnOCR 2 1B 600ms

680ms

720ms

Deploy your way

Managed Cloud

Full compute toolkit for your agents with zero ops.

No idle GPUs, pay for what you use
Fits your stack: SDK, API, CLI, MCP
Zero lock-in, self-host the same stack
SOC 2 Type 2, US or EU data residency

$50 Example credit balance

1.3B tokens embedded

120k pages parsed

27M tokens generated (1:1 in:out)

Get started

no credit card required

Self-host with K8s

Easy & scalable deployment in your own cloud.

Terraform to your cloud in minutes
Apache-2.0, same engine as Cloud
Scales to zero, no bill between jobs
Per-tenant pools, no noisy neighbors

Agent prompt

Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deploymentDeploy SIE to our GCP project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deploymentDeploy SIE to our Azure AKS cluster via helm install. Requirements: superlinked.com/docs/deployment

Deploy guide

Run locally

Run the same models on your own machine.

Runs on NVIDIA GPU or Apple Silicon
One command, no Docker or cluster
All 100+ Cloud models, fully offline
Same SDK and IDs, no code changes

pip install "sie-server[local]" && sie-server servepip install "sie-server[local]" && sie-server serve --device cuda

Quickstart

Run OCR with one extract call

OCR quality and latency

Deploy your way