Skip to content
#

mnist-handwriting-recognition

Here are 520 public repositories matching this topic...

OCR_Mnist_Digits

Demonstration of simple handwritten digit recognition using a neural network in Python. Based on a book by Tariq Rashid. The neural network is able to decipher greyscale 28 x 28 pictures of numerical digits 0-9 with a very high success rate. It uses MNIST data for training and testing but can also be used with other similar data.

  • Updated Aug 26, 2020
  • Python

Gaussian naive Bayes classifier for digits in the MNIST dataset. Similar in nature to my other repo ("newsgroup-naive-bayes"), albeit instead of multinomial document classification, this repo explores gaussian image classification. Covariance smoothing utilized to minimize error rates to the ~4% realm.

  • Updated Apr 14, 2021
  • Jupyter Notebook

This project takes dataset from MNIST which contains (28 x 28) pixel images of 0-9 digits. I have trained a model which is an improvement of output softmax activation function. All the implemented layers are dense. Neural network contains 3 layers with 128,128 and 10 neurons respectively

  • Updated Aug 21, 2023
  • PureBasic

Improve this page

Add a description, image, and links to the mnist-handwriting-recognition topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the mnist-handwriting-recognition topic, visit your repo's landing page and select "manage topics."

Learn more