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Automate Text Labeling for Your Image Dataset: A Step-by-Step Guide

June 28, 2024
5 mins
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Building a high-quality image dataset can be a daunting task, especially when it involves extensive manual labeling. Fortunately, with the Encord Agents, you can automate the process of text labeling, making your workflow more efficient and accurate.


In this blog, we'll walk you through how to set up and use Encord Agents to perform OCR, streamlining your image annotation tasks.



Why Use OCR for Text Labeling?

OCR enables the extraction of text from images, transforming it into editable and searchable data. This can be incredibly useful for labeling datasets that contain images with embedded text, such as street signs, documents, product labels, and more. By automating this process with Encord Agents, you can save time and ensure consistency in your annotations.



Using Encord to Automate Text Labeling

Uploading Data

The first step of any data labeling process is data curation. We will upload our data to Encord Index which streamlines this process by enabling data collection, versioning, and quality assurance. 

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Here, you have the option to upload your data directly or seamlessly integrate with leading cloud storage providers such as AWS S3, Azure Blob Storage, Google Cloud Storage, and Open TelekomCloud OSS.


Set Up Encord Agent

Define Task

First, determine the specific task you want your Encord Agent to perform. For this example, we'll focus on using OCR to extract and label text from images.


Set Up a Server

You'll need a server to run your code. This could be an AWS Lambda function, a Google Cloud function, or any server that supports HTTPS endpoints. 


Register the Agent in Encord

Next, you'll need to register your OCR Agent in Encord. Encord will send a payload that includes necessary details like project hash, data hash, and image URL. 

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In Encord Apollo, navigate to the Agents section and select Register Agents. Here, enter the name, description, and endpoint of the agent to complete the registration process.


Test the Agent

After registration, test your Agent before using it in Label Editor.

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Let’s start labeling!


Automated Data Labeling

Start your Annotation Project. In this example, we are annotating road signs. Trigger the Agent in the Label Editor of Encord Annotate to get the OCR text to add to the label.


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By automating text extraction from images, it saves time and ensures consistency in labeling. This automation reduces manual effort, allowing annotators to focus more on refining annotations rather than repetitive data entry tasks.



Encord Agents are crucial in automating data labeling processes. By integrating technologies like GPT-4o, Gemini, BERT, T5, and other state-of-the-art models, Encord Agents allows users to achieve better accuracy and productivity in data annotation workflows. Whether you're annotating images, documents, or videos, these agents streamline the labeling process, allowing annotators to focus on refining annotations rather than repetitive tasks. This integration not only enhances workflow efficiency but also ensures consistent and high-quality annotations throughout your projects.








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Written by
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Akruti Acharya

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