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PaddleOCR-VL is an ultra-compact 0.9B parameter vision-language model for document parsing, supporting 109 languages with text, table, formula, and chart recognition in JSON or Markdown output. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

image-to-text
Input

Idle

$0.01per run·~100 / $1

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README

WaveSpeedAI PaddleOCR

Extract text from images with WaveSpeedAI PaddleOCR — a fast, accurate optical character recognition model. Simply upload an image and get clean, structured text output in JSON or Markdown format. Perfect for document digitization, data extraction, and text recognition tasks.

Why It Works Great

  • High accuracy: Powered by PaddleOCR for reliable text recognition.
  • Multi-language support: Recognizes text in multiple languages.
  • Flexible output: Choose between JSON or Markdown format.
  • Document-friendly: Handles scanned documents, screenshots, and photos.
  • Ultra-affordable: Just $0.005 per image.
  • Fast processing: Quick turnaround for high-volume workflows.

Parameters

ParameterRequiredDescription
imageYesImage containing text (upload or public URL).
output_formatNoOutput format: json or markdown. Default: markdown.

How to Use

  1. Upload your image — drag and drop or paste a public URL.
  2. Choose output format — select JSON for structured data or Markdown for readable text.
  3. Run — click the button to process.
  4. Copy or download — use the extracted text as needed.

Pricing

$0.005 per image.

Output Formats

FormatDescriptionBest For
markdownClean, readable text with formattingDocuments, articles, readable output
jsonStructured data with position infoData processing, integration, automation

Best Use Cases

  • Document Digitization — Convert scanned documents to editable text.
  • Data Extraction — Pull text from invoices, receipts, and forms.
  • Screenshot Text — Extract text from screenshots and images.
  • Business Cards — Digitize contact information quickly.
  • Batch Processing — Process large volumes of documents affordably.
  • Content Migration — Convert image-based content to text format.

Supported Content Types

  • Scanned documents (PDF pages, printed text)
  • Screenshots and screen captures
  • Photos of documents and signs
  • Handwritten text (with varying accuracy)
  • Multi-column layouts
  • Tables and structured content

Pro Tips for Best Results

  • Use high-resolution images for better accuracy.
  • Ensure good contrast between text and background.
  • Straighten skewed documents before processing.
  • Use JSON format when you need text positions or bounding boxes.
  • Use Markdown format for clean, human-readable output.
  • At $0.005 per image, batch processing is extremely cost-effective.

Notes

  • If using a URL, ensure it is publicly accessible.
  • Processing time is typically under a second per image.
  • Accuracy depends on image quality and text clarity.
  • Supports multiple languages and character sets.
Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

Paddle Ocr API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/paddle-ocr with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Paddle Ocr below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "output_format": "markdown"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/paddle-ocr" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/paddle-ocr";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "output_format": "markdown"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "output_format": "markdown"
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/wavespeed-ai/paddle-ocr", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

Paddle Ocr API — Frequently asked questions

What is the Paddle Ocr API?

Paddle Ocr is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. PaddleOCR-VL is an ultra-compact 0.9B parameter vision-language model for document parsing, supporting 109 languages with text, table, formula, and chart recognition in JSON or Markdown output. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Paddle Ocr API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/paddle-ocr.

How much does Paddle Ocr cost per run?

Paddle Ocr starts at $0.010 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Paddle Ocr accept?

Key inputs: `image`, `enable_sync_mode`, `output_format`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/paddle-ocr.

How long does Paddle Ocr take to generate?

Median end-to-end generation time on WaveSpeedAI is around 26 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Paddle Ocr outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

Paddle Ocr | AI Image Understanding API on WaveSpeedAI