Florence-2 Large · OCR with Region
Integréieren
florence-2-large/ocr-with-region
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/florence-2-large/ocr-with-region?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/florence-2-large/ocr-with-region?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/florence-2-large/ocr-with-region", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
# 1. Connect once (Claude Code; Cursor, VS Code, Claude Desktop: https://doitong.com/lb/developers/docs/mcp)
claude mcp add --transport http doitong-models https://api.doitong.com/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "florence-2-large/ocr-with-region",
"input": {
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
}
Rufft de Florence-2 Large · OCR with Region-Modell (florence-2-large/ocr-with-region) iwwer d'Doitong API op. Dokumentatioun fir Agenten: https://doitong.com/lb/developers/models/florence-2-large/ocr-with-region.md
POST https://api.doitong.com/v1/run/florence-2-large/ocr-with-region?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
Wann de Äntwertstatus "queued" oder "running" ass, frot GET https://api.doitong.com/v1/requests/REQUEST_ID?wait=20 of, bis en "succeeded" ass; d'Dateie stinn an outputs[].url. POST https://api.doitong.com/v1/quote/florence-2-large/ocr-with-region mam selwechte Body gëtt de geneeë Präis virun der Ausféierung zréck.
Dës Ufro: ≈0,004 $ 4 API-Kreditter Op dëser Säit ausféieren
Ären neien API-Schlëssel. Si gëtt nëmmen elo ugewisen a steet schonn am Code an am API-Schlëssel-Feld hei drënner. Halt se geheim: Setz se ni an Code, deen an engem Browser leeft.
Äin éischt Resultat kënnt hei hin
Baut hei drënner eng Ufro a féiert se mat Ärem API-Schlëssel aus. Output gëtt an eis Späicherung kopéiert, fir ëmmer oder 7 Deeg gratis gespäichert.
Eng Ufro erstellen
D'Kontrolle ginn aus dem Modellschema generéiert. De Code an de Präis aktualiséiere sech, wann s du se ännerst.
/run/florence-2-large/ocr-with-region?wait=20
Präis → 0,004 $
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/florence-2-large/ocr-with-region?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/florence-2-large/ocr-with-region?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/florence-2-large/ocr-with-region", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "run",
"arguments": {
"model_id": "florence-2-large/ocr-with-region",
"input": {
"image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
},
"wait_seconds": 20
}
}
}
Dës Ufro ausféieren
Äre Schlëssel bleift an dësem Browser: e gëtt nëmmen un d'Doitong API geschéckt an nëmme gespäichert (localStorage), wann s du „Erënneren“ aktivéiers. En API-Schlëssel kréien
Iwwer Florence-2 Large · OCR with Region
Féiert OCR op `image_url` duerch a gëtt strukturéiert `results` zeréck, déi den erkannten Text mat Bildregiounen verknëppen, zesumme mat engem veraarbechte Bild, dat dës Regioune weist. Benotzt et, wann de Layout wichteg ass, zum Beispill fir Etiketten oder Schëlter ze lokaliséieren. Fir nëmmen Text ass den einfache OCR-Modell méi unkomplizéiert.
Parameteren
Schéck entweder den kanoneschen Numm oder den natierlechen Numm, net béid. Kanonesch Nimm
image_urlobligatoresch
image
Fichierparameter akzeptéieren eng ëffentlech https:// URL, eng data: URI bis zu 10 MB oder de file_url vun engem Upload. Fichieren
Output
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "florence-2-large/ocr-with-region",
"status": "succeeded",
"created_at": "2026-10-20T12:00:00Z",
"started_at": "2026-10-20T12:00:02Z",
"completed_at": "2026-10-20T12:00:09Z",
"queue_position": null,
"cost": {
"credits": 4,
"usd": "0.004",
"charged": 4,
"refunded": 0
},
"input": {
"image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
},
"output": {
"results": {
"quad_boxes": [
{
"x": 1.0,
"y": 1.0,
"w": 1.0,
"h": 1.0,
"label": "string"
}
]
},
"image": {
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_name": "0-output.jpeg",
"file_size": 1843200,
"width": 1024,
"height": 768
}
},
"outputs": [
{
"type": "image",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_size": 1843200,
"width": 1024,
"height": 768,
"stored": true
}
],
"error": null,
"metrics": {
"inference_seconds": 6.8
},
"urls": {
"self": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
Output-Felder
- results
- object {quad_boxes}Resultater, déi vum Modell zeréckgi ginn.
- image
- object {url, content_type, file_name, file_size, width, height}Bild, dat aus der Veraarbechtung entsteet.
Aner Florence 2 Large-Varianten
Méi Modeller: Bildbearbechtung
Fir AI-Agenten: dës Säit als Markdown ass ënner https://doitong.com/lb/developers/models/florence-2-large/ocr-with-region.md. All Modeller a Guiden: https://doitong.com/developers/llms.txt






