OCR.chat Säiten

Eng HTTP-Ufro transforméiert e Bild oder eng PDF an e reinen Text, Markdown, Tabellen a JSON - an iwwer 100 Sproochen. Pro Säit bezuelt, keng Iwwerraschungen.

Iwwersiicht

The OCR.chat API is a small REST interface. You POST a file and get back a job with the recognized text and a per-page breakdown (text, bounding boxes, confidence). Jobs of 5 pages or fewer return inline; larger jobs return immediately with a pending status that you poll until done.

  • Base URL: https://ocr.chat
  • Formats in: PNG, JPG, WEBP, GIF, BMP, TIFF, and multi-page PDF
  • Formats out: txt, md, docx, pdf, csv, json
  • Engines: cpu (fast, printed docs) and vlm (premium AI, handwriting, complex layout, math)

Authentifizéierung

Authenticate with your API token (find it on your account page) as a Bearer header:

Authorization: Bearer YOUR_API_TOKEN

You can also pass ?api_token=… as a query parameter. Usage is metered against your account's page balance.

Dokument ausféieren

POST /api/v1/ocr/, multipart form upload.

curl -X POST https://ocr.chat/api/v1/ocr/ \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -F "file=@invoice.pdf" \
  -F "tier=vlm" \
  -F "language=auto"

Returns the job. For ≤5-page files it is already done with the text; larger files come back pending/processing, poll the status endpoint.

{
  "uuid": "9f2c1b7e4a...",
  "status": "done",
  "tier": "vlm",
  "language": "auto",
  "page_count": 1,
  "mean_confidence": 0.98,
  "text": "INVOICE\nAcme Corp\nTotal: 215.00 USD",
  "markdown": "# INVOICE\n\n**Acme Corp** ...",
  "pages": [ { "index": 0, "text": "...", "blocks": [ { "text": "...", "bbox": [x0,y0,x1,y1], "confidence": 0.98 } ] } ]
}

Resultat kréien

GET /api/v1/ocr/<uuid>/, poll until status is done or failed.

curl https://ocr.chat/api/v1/ocr/9f2c1b7e4a.../ \
  -H "Authorization: Bearer YOUR_API_TOKEN"

Format erofgelueden

GET /api/v1/ocr/<uuid>/download/?format=md, export the result. format is one of txt, md, docx, pdf, csv, json.

curl -L "https://ocr.chat/api/v1/ocr/9f2c1b7e4a.../download/?format=docx" \
  -H "Authorization: Bearer YOUR_API_TOKEN" -o result.docx

Chat mat engem Dokument

Froen iwwer en ofgeschlossen Job stellen. D'Äntwerten hänken nëmmen vum extrahéierte Text of an zitéieren d'Quellsäit. Et ass e Kont-Token néideg - d'Chat-Funktioun ass Kont-gekoppelt.

POST /api/v1/chat/<uuid>/, JSON body {"message": "your question"}.

curl -X POST https://ocr.chat/api/v1/chat/9f2c1b7e4a.../ \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"message": "What is the invoice total and due date?"}'

Gitt d' Assistent- Noriicht mat der Äntwert zréck an eng Lëscht vun de zitéierte Säiten:

{"conversation": "a1b2…", "message": {
   "role": "assistant",
   "content": "The total is $42, due on March 3 (p. 1).",
   "citations": [{"page": 1, "snippet": "The invoice total is $42…"}]
}}

GET /api/v1/chat/<uuid>/history/, % 1:% 2,% 3,% 4,% 5,% 6

Code-Beispill

import requests, time

API = "https://ocr.chat/api/v1/ocr/"
H = {"Authorization": "Bearer YOUR_API_TOKEN"}

# Submit
with open("invoice.pdf", "rb") as f:
    job = requests.post(API, headers=H,
        files={"file": f}, data={"tier": "vlm"}).json()

# Poll until done
while job["status"] in ("pending", "processing"):
    time.sleep(2)
    job = requests.get(API + job["uuid"] + "/", headers=H).json()

print(job["markdown"])

# Download as DOCX
r = requests.get(API + job["uuid"] + "/download/",
                 headers=H, params={"format": "docx"})
open("result.docx", "wb").write(r.content)
import fs from "fs";

const API = "https://ocr.chat/api/v1/ocr/";
const H = { Authorization: "Bearer YOUR_API_TOKEN" };

const form = new FormData();
form.append("file", new Blob([fs.readFileSync("invoice.pdf")]), "invoice.pdf");
form.append("tier", "vlm");

let job = await (await fetch(API, { method: "POST", headers: H, body: form })).json();

while (["pending", "processing"].includes(job.status)) {
  await new Promise(r => setTimeout(r, 2000));
  job = await (await fetch(API + job.uuid + "/", { headers: H })).json();
}
console.log(job.markdown);
# 1. Submit
curl -X POST https://ocr.chat/api/v1/ocr/ \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -F "file=@invoice.pdf" -F "tier=vlm"

# 2. Poll  (use the uuid from step 1)
curl https://ocr.chat/api/v1/ocr/UUID/ \
  -H "Authorization: Bearer YOUR_API_TOKEN"

# 3. Download
curl -L "https://ocr.chat/api/v1/ocr/UUID/download/?format=md" \
  -H "Authorization: Bearer YOUR_API_TOKEN" -o result.md

Parameter

FieldTypeDescription
filefileRequired. The image or PDF to process.
tierstringcpu (default, fast/printed) or vlm (premium AI: handwriting, layout, math).
languagestringauto (default) or a language code (en, ch, ja, ar, …).
toolstringOptional tool slug (e.g. extract-tables, handwriting-to-text) to apply that tool's preset.
translate_tostringFor the translate tool, target language code.

Fehler & Grenzen

CodeMeaning
400No file, unsupported type, or file too large.
401Missing or invalid API token.
402Out of pages, daily/monthly free limit reached, or no credits. The body includes used/cap.
404Job UUID not found.
409Download requested before the job finished.

Each page processed costs credits (1/page on the fast tier, more on premium). Paid plans raise per-file page caps and add priority. See pricing.

Häufig gestallt Froen

Create a free account and open your account page, your token is shown there with a copy button.

Yes, files of 5 pages or fewer return the full result inline in the POST response, so no polling is needed for most images and short PDFs.

Over 100, including Latin, CJK, Arabic, Cyrillic and Indic scripts. Use language=auto to detect, or pass a specific code.

Uploads are processed for OCR and deleted automatically. We never sell, share, or train on your documents.

D'Benotzung gëtt pro Säit géint Äre Kontbilanz gemeet: anonyme Rufe kréien eng pro IP all Dag, gratis Konts e monatlecht Bucket, a bezuelte Pläng benotzen kaaft Kreditter mat héijer pro Datei Säiten Limiten an Prioritéit. Wann Dir aus ass kritt Dir e 402 mat benotzt an Limit am Kierper.

Dir kënnt PNG, JPG, WEBP, GIF, BMP, TIFF an eng méisäiteg PDF schécken. Resultater kënne wéi txt, md, docx, pdf (sichbar), csv oder json iwwer de Formatparameter vum Download-Ennpunkt erofgelueden ginn.

400 ass eng Datei déi net do ass, e Typ deen net ënnerstëtzt gëtt oder déi ze grouss ass; 401 ass e fehlend oder ongülteg Token; 402 ass net méi op der Säit; 404 ass eng onbekannt UUID; an 409 ass en Download deen virum Ofschloss vum Job ugefrot gouf. D' Feelertexter enthalen eng kuerz Meldung.

En Aufgabobjekt mat Status, Tier, Sprooch, page_count an mean_confidence, plus dem vollen Text an Markdown. D'Säit-Array bréngt all Säit an Blöcke mat hirem Text, Boundingbox (bbox) an pro-Block Confiance.

Benotzt CPU (standard) fir eng séier, niddreg-Käschte Erkennung vu sauberen gedréiten Dokumenter. Benotzt vlm, d'Premium AI Engine, fir Handschreiwen, komplex oder méi Spalten Layouts, Mathematik, an Iwwersetzungen, wou et vill méi genee ass.

Passéiert dem Tool e Schlësselwierder (z. B. extract-tables oder handwriting-to-text) fir d'Vireinstellungen vum Tool unzewenden. Fir d'Ënnerstëtzungs-Tool, passéiert och translate_to mat dem Zilsproochcode fir den erkannten Text zréck ze iwwersetzen.

Dateien mat 5 Säiten oder manner ginn an der POST-Antwort inline zréck. Grouss Dateien kommen direkt zréck als an der Warteschleife oder am Prozess, an Dir frot GET /api/v1/ocr/<uuid>/ bis de Status fäerdeg ass oder net. Bezuelt Pläng erhéijen d' Säitenoptioun pro Datei.

D'API ass einfach REST iwwer HTTPS, sou datt et vun all Sprooch mat engem HTTP Client funktionnéiert - kuckt d'Python, Node.js, an cURL Beispiller uewen. Et gëtt keng SDK ze installéieren; e puer Linnen vun standard HTTP Code sinn alles wat Dir braucht.