✋ Hand Digit Classifier - Real-Time AI Recognition 🚀

✨ Key Features

🏆 Core Capabilities

Feature	Description	Technology Used	Performance
🎥 Live Digit Capture	Real-time webcam hand digit recording	OpenCV	30+ FPS
🧠 CNN Classification	High-accuracy digit recognition	TensorFlow	96% Accuracy
⚡ Low-Latency	Fast processing pipeline	Python	<20ms/frame
📊 Visual Feedback	On-screen predictions & FPS counter	OpenCV	18ms render
📦 Easy Setup	One-command installation	pip	<1min

📊 Performance Metrics

Metric	Value	Details
Test Accuracy	96.2%	MNIST test set
Inference Speed	8ms	NVIDIA RTX 3060
CPU Usage	35-45%	Intel i7-11800H
Memory Consumption	380MB RAM	During operation
Model Size	2.1 MB	Optimized TensorFlow Lite

🔍 Feature Comparison

Capability	This Project	Alternative Solutions
Real-Time Processing	✅ Yes	❌ No
Webcam Support	✅ Yes	❌ Limited
Pretrained Model	✅ Included	❌ Requires Download
Open Source License	✅ MIT	❌ Proprietary
Multi-Platform	✅ Win/Linux	❌ Windows-only

⏱️ Processing Pipeline

Stage	Time Taken	Tools Used
1. Frame Capture	2ms	OpenCV VideoCapture
2. Preprocessing	4ms	NumPy, OpenCV
3. CNN Inference	8ms	TensorFlow Lite
4. Visualization	4ms	OpenCV
Total Latency	18ms

Note: All metrics measured on Intel i7-11800H + RTX 3060 system at 640x480 resolution

🛠️ Tech Stack

🧠 CNN Architecture

graph TD

A[Input Layer\n28x28 Grayscale] -->|Preprocessed\nImage| B[Conv2D\n32 filters]

B --> C[ReLU Activation]

C --> D[MaxPooling2D\n2x2]

D --> E[Conv2D\n64 filters]

E --> F[ReLU Activation]

F --> G[MaxPooling2D\n2x2]

G --> H[Flatten]

H --> I[Dense\n128 neurons]

I --> J[Dropout\n0.5]

J --> K[Output Layer\n10 neurons]

K --> L[Softmax\nClassification]

  

style A fill:#5e81ac,stroke:#4c566a,color:white

style B fill:#88c0d0,stroke:#4c566a

style C fill:#81a1c1,stroke:#4c566a

style D fill:#8fbcbb,stroke:#4c566a

style E fill:#88c0d0,stroke:#4c566a

style F fill:#81a1c1,stroke:#4c566a

style G fill:#8fbcbb,stroke:#4c566a

style H fill:#d08770,stroke:#4c566a

style I fill:#ebcb8b,stroke:#4c566a

style J fill:#e5e9f0,stroke:#4c566a

style K fill:#a3be8c,stroke:#4c566a

style L fill:#b48ead,stroke:#4c566a

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🚀 Quick Start

1. Install & Run

git clone https://github.com/codewithcc/hand-digit-classifier.git

cd hand-digit-classifier

pip install -r requirements.txt

python main.py

📊 Performance Metrics

Model Accuracy & Speed

pie title Test Set Accuracy (MNIST)

"Correct Predictions" : 96

"Incorrect Predictions" : 4

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