How to build "Handwritten Letters and Digits Recognition with CNN in Python" | Deep Learning Project
🎥 Real-Time Handwritten Digit Recognition Using Python & CNN (MNIST)
Welcome to this exciting deep learning project where we build a real-time handwritten digit recognition system using Python, PyTorch, OpenCV, and Tkinter! 🚀
This project captures digits using your webcam, processes the images, classifies them using a Convolutional Neural Network (CNN) trained on the MNIST dataset, and even reads the result aloud with Text-to-Speech (TTS)! 🔊
🧠 What You'll Learn in This Video:
How to build a CNN from scratch using PyTorch
How to train on the MNIST dataset
Real-time webcam integration using OpenCV
Image preprocessing (grayscale, thresholding, resizing)
GUI creation with Tkinter
Using Text-to-Speech (pyttsx3) in Python
Applying majority voting on multiple frames to improve prediction accuracy
📁 Tech Stack & Libraries Used:
PyTorch (CNN + MNIST)
Torchvision
OpenCV
Tkinter
pyttsx3
PIL (Image handling)
✅ Features:
Real-time webcam digit recognition
3-frame capture with majority voting
Preprocessing pipeline for better accuracy
Voice output of predictions
Lightweight and user-friendly GUI
📌 Model Architecture:
2 Convolutional Layers + MaxPooling
Fully Connected Layers
Softmax output over 10 classes (digits 0–9)
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