AWS Rekognition Service Demo

📖 Getting Started

Choose Input Method

Select either "Image URL" or "Upload File" tab to provide an image for analysis.

Example URL: https://example.com/photo.jpg

Select Detection APIs

Choose which AWS Rekognition APIs to run on your image:

• DetectLabels: Identifies objects, scenes, and concepts
• DetectFaces: Analyzes facial attributes and features
• DetectText: Extracts text from images (OCR)
• RecognizeCelebrities: Identifies public figures

Analyze Image

Click the "Analyze Image" button to process the image using selected APIs. Results will appear on the right.

Review Results

Examine detection results with confidence scores. Each result includes:

• Detected label/text/face
• Confidence percentage (0-100%)
• Additional attributes (age, emotions, etc.)

💡 Learning Tip Try analyzing different types of images (portraits, documents, scenes) to understand how each API performs across various use cases.

🎯 Real-World Use Cases

• Content moderation
• Document processing
• Face verification
• Visual search

📤 Input Configuration

Tip: Upload an image or provide a URL to analyze using Rekognition APIs.

Enter Image URL

📁 Drop image here or click to browse

Detection APIs

DetectLabels

DetectFaces

DetectText

RecognizeCelebrities

📊 Detection Results

👆 Upload an image or enter a URL above to see detection results

🚀 About Amazon Rekognition

Object & Scene Detection

Identifies thousands of objects, scenes, and concepts in images with high confidence scores.

Face Detection & Analysis

Detects faces and analyzes facial attributes like age range, emotions, and facial landmarks.

Text in Image (OCR)

Extracts text from images including documents, signs, and labels with spatial information.

Celebrity Recognition

Identifies celebrities and public figures in images against a maintained database.

Confidence Metrics

Each detection includes confidence scores (0-100%) indicating detection reliability.

Real-time Processing

Processes images synchronously with low-latency responses suitable for real-time applications.