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

GAN to generate number images

classify handwritten digits (0-9)

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

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

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

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

Digit recognition with tensorflow

Classification of digits in MNIST dataset

Domain Adaptation for digits classification using Deep Reconstruction-Classification Network

A Simple Handwritten Digits Classication

Sudoku solver using OpenCV’s DNN

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