AI-Based Writing Detection System

Real-time monitoring of writing activity using ESP32-CAM and Firebase

Project Description

This project leverages the power of IoT and AI to create a real-time writing detection system. An ESP32-CAM microcontroller captures images periodically, uploading them securely to Firebase Storage. A Convolutional Neural Network (CNN) model, trained and potentially hosted on platforms like Google Colab or Kaggle, analyzes these images to determine if the person in view is actively writing or simply sitting idle. The detection results are updated in the Firebase Realtime Database, enabling various downstream applications like monitoring or alerts.

How It Works

Capture Image

ESP32-CAM takes a picture.

Upload to Firebase

Image sent to Firebase Storage.

AI Analysis

Model classifies image as "Writing" or "Idle".

Update Database

Status stored in Firebase RTDB.

Alert (Optional)

Notification triggered if needed.

Detection Results

Real-time status updates from the Firebase Realtime Database.

TimestampImage PreviewResult
2024-07-28 10:05:15
Preview
Writing
2024-07-28 10:05:30
Preview
Idle
2024-07-28 10:05:45
Preview
Writing

(Sample data shown. Connect to Firebase for live updates.)

Technologies Used
ESP32-CAM MB
Firebase Realtime Database
Firebase Storage
Google Colab (Python)
Keras/CNN Model
Next.js
Tailwind CSS

About the Author

This project was developed by an enthusiastic ECE student exploring the intersection of AI and IoT.

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