This project shows how to use CNN to perform Chinese character recognition, a much more complicated task compared to MNIST digit recognition.

This CNN-based model for recognition of hand written digits attains a validation accuracy of 99.2% after training for 12 epochs. Its trained on the MNIST dataset on Kaggle.

The MNIST database of handwritten digits is one of the most popular image recognition datasets. It contains 60k examples for training and 10k examples for testing.

Handwritten digit recognition with MNIST & Keras

Handwritten digit recognition using neural network trained on 60000 images from MNIST dataset

NumberRecognition is a project aimed at recognizing handwritten digits from the MNIST dataset using PyTorch. It includes scripts for training and inference, along with utilities for dataset preparation. This project is ideal for learning the basics of neural networks and digit recognition. This is a "Hello World" program in the field of AI.

In-Browser Digit recognition with Tensorflow.js and React using Mnist dataset

handwritten digit recognition system using a custom neural network architecture. Built with C, it features both training capabilities and an interactive recognition interface. >98% accuracy on the MNIST dataset.

Handwritten digit recognition is a fundamental problem in the field of computer vision and machine learning. In this project, we propose to implement the LeNet5 model using PyTorch to recognize handwritten digits from the MNIST dataset.

Handwritten Digit Recognition using Convolutional Neural Networks in Python with Keras on live camera

Real time MNIST digit recognition with OpenCV and Support Vector Machine (SVM) algorithm.

Lenet for MNIST handwritten digit recognition using Vivado hls tool

A Spiking Neural Network Architecture for MNIST Digit Recognition

Digit Recognizer for MNIST Data Set

Implementing CNN for Digit Recognition (MNIST and SVHN dataset) using PyTorch C++ API

Implementing Deep learning in R using Keras and Tensorflow packages for R and implementing a Multi layer perceptron Model on MNIST dataset and doing Digit Recognition

Deep Learning with Tensorflow/Keras: Digit recognition based on mnist-dataset and convolutional neural-network on iOS with CoreML

A Hand Written Digit Recognition app trained on the MNIST dataset of Keras using the CNN model. skills used are Tensorflow, HTML,CSS,javascript.

Implementation of a digit recognition using my Neural Network with the MNIST data set.

A pytorch implementation of CNN+BLSTM+CTC to recognize MNIST digit sequence

A Spiking Neural Network model for Digit Recognition using the N-MNIST dataset.

Qt C++ Neural network for MNIST digits recognition

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A complete handwritten digit recognition system using Scikit-learn on the MNIST dataset, featuring model training, evaluation, error analysis, and Gradio app deployment.

A 3-layer SNN code for performing MNIST handwritten digit recognition using a supervised spike based learning rule

✏️ An AI-driven web app for handwritten digit recognition using the MNIST dataset. It leverages TensorFlow for deep learning model training and Gradio to create an intuitive, interactive UI. Users can draw digits and receive instant predictions, showcasing practical AI deployment and real-time inference capabilities.

Keras Fully Connected Neural Network using Python for Digit Recognition

The training dataset consists of 42000 rows each of 784 pixel values thus representing 28 x 28 sized 42000 images of different digits from 0 to 9 . I have used Convolutional Neural Networks to train the model with the help of Keras and made predictions on the 28000 images of the test dataset, also achieved 99.321 % valid accuracy with just 10 epochs . Also tuned ImageDataGenerator to promote generalization and avoid overfitting problem .

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Implementing MNIST(Handwritten digit recognition) Using Various Deep Learning Methods And Libraries.