Handwritten Digit Recognition Using Deep Learning :
This project implements a Handwritten Digit Recognition System using Deep Learning techniques. The model is trained to recognize and classify handwritten digits from 0 to 9 using image data.
The system uses a Convolutional Neural Network (CNN), which is highly effective for image processing and computer vision tasks. The model learns patterns from handwritten digit images and predicts the correct digit with high accuracy.
This project demonstrates the practical application of:
Deep Learning
Computer Vision
Image Classification
Features :
Recognizes handwritten digits (0–9)
Uses Convolutional Neural Networks (CNN)
Trained on image dataset (e.g., MNIST)
Image preprocessing and normalization
Model training, validation, and testing
Accuracy and loss visualization
Predicts custom handwritten digit images
Technologies Used:
Python
TensorFlow / Keras (or PyTorch if you used it) :
NumPy
Matplotlib
OpenCV (if used for image handling)
Jupyter Notebook / Python Scripts
How It Works :
The dataset of handwritten digits is loaded.
Images are preprocessed (reshaped, normalized).
A CNN model is built with convolution, pooling, and dense layers.
The model is trained on training data.
Performance is evaluated on test data.
The model predicts digits from new handwritten images.
Objective : The goal of this project is to build a deep learning model capable of accurately recognizing handwritten digits, which is a fundamental problem in optical character recognition (OCR) and forms the basis for many real-world AI applications like document processing and digitizing handwritten forms.