Aim of the POC:
The aim of this Proof of Concept (PoC) is to demonstrate the implementation of Azure Computer Vision Services for text extraction from images and image categorization. This PoC will guide you through the steps to extract text from images and categorize them using Azure Computer Vision Services.
Azure Computer Vision:
Azure Computer Vision is a specific service within Microsoft’s Azure Cognitive Services suite. It provides powerful and versatile capabilities for analyzing and extracting information from images and videos. Azure Computer Vision is designed to make it easier for developers to build applications that can understand and interpret visual content.
Key features of Azure Computer Vision include:
1. Optical Character Recognition (OCR): Azure Computer Vision can extract printed and handwritten text from images and documents. This is particularly useful for digitizing paper documents, performing text analysis, and improving accessibility.
2. Image Analysis: It can analyze the content of images to identify objects, faces, landmarks, and more. This feature enables applications to categorize and understand visual content.
3. Content Moderation: This feature can automatically detect and filter out content that may be inappropriate, offensive, or violate content guidelines. It’s commonly used in social media and user-generated content platforms.
4. Face Detection: It can identify faces in images and provide information about facial attributes, such as age, gender, and emotion. This is valuable for building applications like facial recognition systems and sentiment analysis.
5. Object Detection: Azure Computer Vision can detect and categorize objects within an image, making it useful for applications like inventory management and image-based search.
6. Handwriting Recognition: The service supports the recognition of handwritten text, which is beneficial for applications like digitizing handwritten notes and forms.
7. Custom Vision Models: You can create and train custom models for specific image classification and object detection tasks using your own datasets.
Azure Computer Vision offers both a REST API and SDKs for various programming languages, making it accessible to developers working in diverse environments. It can be used in a wide range of applications, from content management systems to e-commerce, healthcare, and industrial automation. It helps businesses and developers extract valuable insights and automate tasks from visual data.
Implementation Steps:
Step 1. Setting up Azure Computer Vision resource.
Create a Computer Vision resource from the create a resource on the Azure portal. Fill all the required details in the resource and select the pricing as F0 as this is the free tier.
After Creating the resource go to the resource and note down the subscription key and Computer Vision API endpoint as we would require this information later.
Step 2. Prepare a Blob Storage.
Create an Azure Blob Storage account.
Keep in mind to enable anonymous access while creating the storage account.
After the Successful Creation of the Storage Account.
Create a new container in the Storage account.
And allow anonymous access while selecting the container level.
After Creating a container, you can upload the images in the container from which we want to extract the text.
Ensure to note down the keys and storage account credentials as it will be required later.
Step 3. Install Required Python Libraries.
We can install required python libraries using pip.
Step 4. Python Code for Text Extraction.
Import required dependencies and pass the keys and endpoints of your blob storage and computer vision credentials to make a connection.
This Python code will extract the Text from the images .
Step 5. Python code for Image Categorization.
Import required dependencies and pass the keys and endpoints of your blob storage and computer vision credentials to make a connection.
This Python code will categorize the images.
Step 6. Running the Code and Observing results.
- Running the Text Extraction Code.
We can see that after the code is successfully ran, we are able to extract the text from the images.
- Running the Image Categorization Code.
We can see that after the successful run of code, we are able to categorize the images.
Conclusion:
This PoC demonstrates the successful implementation of text extraction and image categorization using Azure Computer Vision Services. You can use this PoC as a foundation for more advanced applications, such as document analysis, object detection, and content moderation, by extending the capabilities of the Computer Vision API.
By following these steps, you can harness the power of Azure Computer Vision Services to extract valuable information from images and classify them into predefined categories, which can be invaluable for a wide range of applications, from content management to data extraction.