TensorFlow

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.keras as keras
import warnings
warnings.filterwarnings("ignore")

from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, SimpleRNN, Dense, Dropout
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.utils import  plot_model

Introduction

Welcome to the Deep Learning for Digit Classification, This repository is dedicated to exploring the fascinating world of deep learning in the context of digit classification. Digit classification is a fundamental problem in computer vision, and it serves as a building block for many real-world applications, including optical character recognition (OCR) and digit-based data analysis.

About Digit Classification

Digit classification involves the task of recognizing and categorizing handwritten or printed digits into their respective numerical representations (0-9). This seemingly simple task presents unique challenges in computer vision, as digits can vary widely in writing styles, sizes, and orientations.

What You'll Find Here

In this repository, we have curated a collection of deep learning models and approaches designed to tackle the digit classification problem. Each model showcases a different architectural approach, offering a valuable learning experience for those interested in computer vision and deep learning.

Models Included

LeNet: A classic convolutional neural network (CNN) architecture known for its pioneering role in image classification.

Convolutional Neural Network (CNN): A custom-designed CNN architecture tailored for digit recognition on the MNIST dataset.

Recurrent Neural Network (RNN): An exploration into sequence-based digit classification using recurrent neural networks.

Support Vector Classification: An implementation of Support Vector Machines (SVMs) for digit classification, showcasing a different paradigm.