digits-classification

An end to end deep learning project made by using streamlit for predicting handwritten drawn digits/alphabets via CNN & ANN.

Sudoku grid and digits detection

This project is the implementation of the sudoku solver using computer vision & backtracking algorithm

A complete, straightforward digit classification project built with PyTorch, featuring CNN-based training, evaluation metrics, confusion matrix visualization, and XAI using Grad-CAM.

classify handwritten digits (0-9)

GAN to generate number images

Unsupervised domain adaptation using feature aligned maximum classifier discrepancy (FAMCD)

Handwritten Digit Classification (SVM): This project implements a multiclass classification model to recognize handwritten digits (0-9) using a Support Vector Machine (SVM).

A Multi-Layer Perceptron (MLP) implemented from scratch with NumPy, trained to classify handwritten digits (0–9)

Digit recognition (MNIST dataset) using a fully connected neural network (97+ on test)

A project allowing to recognize handwritten numbers in the range 0-9.

Domain Adaptation for digits classification using Deep Reconstruction-Classification Network

Classification of digits in MNIST dataset

Digit recognition with tensorflow

99% accurate CNN for MNIST handwritten digits

Implementing Classical Machine Learning solutions in Python using the Scikit-Learn library and other libraries , specifically classification methods applied to the Digits Dataset.

Comparing Logistic Regression and nonlinear SVM for handwritten digit classification.

Sudoku solver using OpenCV’s DNN

A Simple Handwritten Digits Classication

Digit detection using Singular Value Decomposition