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.

GAN to generate number images

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

classify handwritten digits (0-9)

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

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

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

Classification of digits in MNIST dataset

Digit recognition with tensorflow

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

99% accurate CNN for MNIST handwritten digits

Domain Adaptation for digits classification using Deep Reconstruction-Classification Network

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

Sudoku solver using OpenCV’s DNN

A Simple Handwritten Digits Classication

Digit detection using Singular Value Decomposition

Classification of handwritten digits using classical machine learning methods with model optimization and evaluation on the Scikit-learn Digits dataset