How to Build an Image Reader with Gemini OCR and Telegram

Optical Character Recognition (OCR) is a fundamental building block for many automation and AI workflows. With n8n, Google Gemini, and Telegram, you can implement a robust, chat-based image reader that extracts text from images in real time and returns it directly to end users.

This article explains how to assemble a production-ready Image Reader workflow in n8n using the Gemini OCR model and Telegram integration. It covers the overall architecture, node configuration, and recommended best practices for reliability, security, and maintainability.

Solution Architecture

The workflow connects Telegram as the user-facing interface with Gemini OCR as the AI text extraction engine. n8n orchestrates the process, from receiving an image to returning the recognized text.

The automation is built around the following core nodes:

• Telegram Trigger – Listens for incoming Telegram messages and captures images.
• Clean Input Data – Normalizes and extracts relevant fields from the Telegram payload, such as chat ID and file ID.
• Get File – Downloads the actual image file from Telegram using the file ID.
• Extract from File – Converts binary image data to a Base64 string suitable for Gemini OCR.
• Gemini OCR (HTTP Request) – Sends the Base64-encoded image to the Gemini API and retrieves the extracted text.
• Telegram – Returns the OCR result to the originating chat.

Once deployed, users simply send an image to the Telegram bot and receive the detected text as a reply, with no manual file handling or external tools required.

Configuring the Workflow in n8n

1. Telegram Trigger – Entry Point for Images

The Telegram Trigger node is the starting point of the workflow and is responsible for listening to new updates from Telegram.

Key configuration guidelines:

• Update type: Set to message so the node reacts to standard chat messages.
• File handling: Enable the option to download files. This ensures that when a user sends a photo, n8n receives the associated file metadata and can later download the image.

With this configuration, every photo sent to the bot will trigger the workflow and pass along the full message JSON, including photo metadata.

2. Clean Input Data – Extract Chat and Image Metadata

The next step is to simplify the raw Telegram payload and extract the information required for subsequent nodes. This is typically done with a function-like node or an equivalent transformation step that defines custom fields.

At a minimum, capture:

• chatID – The unique Telegram chat identifier used to send the response back to the correct conversation.
• Image – The file ID of the image that you want to process. For photos, Telegram usually provides multiple sizes. You should select the last element in the photo array, which corresponds to the highest resolution version.

By normalizing these fields early, you keep the workflow easier to maintain and reduce the complexity of downstream nodes.

3. Get File – Download the Image from Telegram

Once you have the file ID, use the Get File node (Telegram integration) to download the actual image content.

Configuration recommendations:

• Map the node’s file ID parameter to the Image value produced in the previous step.
• Ensure the node is set to return the file as binary data, which is required for the conversion step.

This node outputs the image in binary format, which is the raw data that will be transformed for Gemini OCR.

4. Extract from File – Convert Binary to Base64

Most modern OCR and vision APIs, including Gemini, expect image content as a Base64-encoded string rather than raw binary. The Extract from File node handles this conversion.

Typical configuration:

• Select the binary property that contains the downloaded image.
• Convert that binary data into a Base64 string and store it in a JSON field, for example data.

After this step, your workflow has a clean JSON object that includes a Base64 representation of the image, ready to be sent to Gemini OCR.

5. Gemini OCR – Call the Gemini API via HTTP Request

The core OCR logic is implemented through an HTTP Request node that calls the Gemini OCR API. This node sends the Base64-encoded image and receives the extracted text as a response.

Configure the HTTP Request node as follows:

• URL: Use the Gemini content generation endpoint, for example:
  https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent
• Method: POST
• Authentication:
  • Type: Generic Credential Type with Query Auth.
  • Create and store your API key credentials securely in n8n.
  • Obtain your Gemini API key from Google AI Studio.
• Body format: JSON

Use a request body similar to the following, mapping the Base64 field from the previous node:

{  "contents": [  {  "role": "user",  "parts": [  {  "inlineData": {  "mimeType": "image/jpeg",  "data": "{{ $json.data }}"  }  },  {  "text": "Extract text"  }  ]  }  ]
}

In this structure:

• mimeType should match the actual image type, such as image/jpeg. Adjust if your use case relies on other formats.
• {{ $json.data }} references the Base64-encoded image generated in the Extract from File node.
• The text part provides an instruction to the model, in this case asking it to “Extract text” from the image.

The node will return Gemini’s response payload, which includes the recognized text that you can parse and forward to the user.

6. Telegram Node – Return the OCR Result to the User

The final step is to send the extracted text back to the originating Telegram chat using the standard Telegram node.

Configuration points:

• Map the chat ID field to the chatID value captured in the Clean Input Data step.
• Set the message text to the OCR output from the Gemini node.
• Disable automatic attribution or footers if you want a clean, minimal response message.

With this in place, the workflow completes the loop: the user sends a photo, the automation extracts the text using Gemini, and the result is returned directly in the same Telegram conversation.

Operational Best Practices

To ensure that this n8n workflow is robust and production-ready, consider the following recommendations.

Bot Permissions and User Experience

• Verify that your Telegram bot is configured to receive photos and other media types required by your use case.
• Optionally, send a short introductory message when users first interact with the bot, explaining that they can upload images for OCR.

Error Handling and Resilience

• Add error handling branches or dedicated nodes to catch failures, such as invalid images, oversized files, or API timeouts.
• Provide clear, user-friendly error messages in Telegram if text extraction fails, for example prompting the user to resend a clearer image.
• Log errors or key metrics internally to monitor API usage and workflow performance.

Security and Credential Management

• Store the Gemini API key exclusively in n8n credentials, not in plain text inside nodes or code.
• Restrict access to your n8n instance and credentials to authorized users only.
• Rotate API keys periodically according to your organization’s security policies.

Conclusion

By integrating Telegram, n8n, and Gemini OCR, you can deliver a powerful, real-time image reader that operates entirely within a familiar chat interface. The workflow outlined here captures images from Telegram, converts them into a Gemini-compatible format, extracts the text, and returns the result to the user with minimal latency.

For automation professionals, this pattern can be extended further, for example by forwarding extracted text to document management systems, databases, or downstream AI pipelines.

Next Steps

Implement this image reader workflow in your n8n environment to streamline image text extraction directly from Telegram. Use it as a foundation for more advanced document processing, compliance checks, or data entry automations.

If this guide was useful, consider sharing it with your engineering or automation team and explore additional n8n workflow templates to expand your automation capabilities.