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+---
+title: How to query OCR models
+description: Learn how to interact with powerful OCR models using Scaleway Generative APIs.
+tags: generative-apis ai-data ocr-models chat-completions-api
+dates:
+  validation: 2025-10-13
+  posted: 2026-10-13
+---
+import Requirements from '@macros/iam/requirements.mdx'
+
+The Scaleway Generative APIs service allows users to interact with powerful OCR (Optical Character Recognition) models hosted on the platform.
+
+<Message type="note">
+  - OCR models are designed specifically to detect and extract text from images. They focus on identifying characters, words, and text layout within an image. 
+  - They are different from [vision models](/generative-apis/how-to/query-vision-models/), which are designed to analyze visual content, such as objects, scenes, and activities. 
+</Message>
+
+There are several ways to interact with OCR models:
+
+- **Scaleway console playground**: The Scaleway [console](https://console.scaleway.com) provides a complete [playground](/generative-apis/quickstart/#interacting-with-generative-apis-via-the-playground) for Generative APIs. This visual interface allows you to test models, adapt query parameters, and observe how these changes affect the output in real-time.
+- **[Chat Completions API](https://www.scaleway.com/en/developers/api/generative-apis/chat-completions)**: Use the Chat Completions API to query OCR models programmatically.
+
+<Requirements />
+
+- A Scaleway account logged into the [console](https://console.scaleway.com)
+- [Owner](/iam/concepts/glossary/#owner) status or [IAM permissions](/iam/concepts/glossary/#permission) allowing you to perform actions in the intended Organization
+- A valid [API key](/iam/credentials/create-api-keys/) for API authentication
+- Python 3.7+ installed on your system
+
+## Access the playground
+
+Scaleway provides a web playground for OCR models hosted on Generative APIs.
+
+1. In the [Scaleway console](https://console.scaleway.com/) side menu, go to **AI** >  **Generative APIs**. The list of models you can query appears. 
+2. Select the **Serverless** toggle button.
+3. Click the name of the OCR model you want to try. Alternatively, click **Try** next to the name of the model.
+
+The web playground opens.
+
+## Use the playground
+
+1. Upload a `.pdf` file to the prompt area at the bottom of the page, or click **Analyze sample document**. The model starts detecting your document and rendering text. 
+2. Scroll down the rendered text to find an estimation of the number of input/output tokens, and the total amount of consumed tokens for this task. 
+3. Click **Use** to see the code snippets configured according to your settings in the playground. 
+
+
+## Query OCR models via the API
+
+You can query OCR models programmatically using your favorite tools or languages.
+
+OCR models take both text and images as inputs.
+
+In the example that follows, we will use the OpenAI Python client.
+
+<Message type="tip">
+  Unlike traditional language models, OCR models take a single file as input. See the `python` example below to understand the expected requested format.
+</Message>
+
+### Install the OpenAI SDK
+
+Install the OpenAI SDK using pip:
+
+```bash
+pip install openai
+```
+
+### Initialize the client
+
+Initialize the OpenAI client with your base URL and API key, and define the file that you want to analyze. For the example, we will use this file: https://genapi-documentation-assets.s3.fr-par.scw.cloud/scaleway-impact-report-3-pages.pdf.
+
+
+```python
+from openai import OpenAI
+import base64
+import os
+import urllib.request
+
+# Define the FILE that the OCR model will analyze. Here we use a public Scaleway document for the example
+FILE = "https://genapi-documentation-assets.s3.fr-par.scw.cloud/scaleway-impact-report-3-pages.pdf"
+
+# Initialize the client with your base URL and API key
+client = OpenAI(
+    base_url = "https://api.scaleway.ai/999d23ea-7afa-485f-9d6c-27db078f1527/v1",
+    api_key = os.getenv"<SCW_SECRET_KEY>" # Replace <SCW_SECRET_KEY> with your IAM API key
+)
+
+with urllib.request.urlopen(FILE) as response:
+    encoded_file = base64.b64encode(response.read()).decode("utf-8")
+```
+
+### Generate a chat completion
+
+You can now create a chat completion:
+
+```python
+response = client.chat.completions.create(
+  model="lightonocr-3-0.8b",
+  messages=[
+    {
+      "role": "user",
+      "content": [{
+        "type": "file",
+        "file": {
+            "file_data": f"data:application/pdf;base64,{encoded_file}"
+        }
+      }
+      ]
+    }
+  ],
+  stream=False,
+)
+
+if response.choices and response.choices[0].message.content:
+    print(response.choices[0].message.content)
+```
diff --git a/pages/generative-apis/menu.ts b/pages/generative-apis/menu.ts
index 163f7fc546..350dbddea9 100644
--- a/pages/generative-apis/menu.ts
+++ b/pages/generative-apis/menu.ts
@@ -30,6 +30,10 @@ export const generativeApisMenu = {
           label: 'Query vision models',
           slug: 'query-vision-models',
         },
+        {
+          label: 'Query OCR models',
+          slug: 'query-ocr-models',
+        },
         {
           label: 'Query embedding models',
           slug: 'query-embedding-models',