an app recognizing written digits. Using Tensorflow Keras and Pygame 🔢 [finished]
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Updated
May 4, 2020 - Jupyter Notebook
an app recognizing written digits. Using Tensorflow Keras and Pygame 🔢 [finished]
pandas, sklearn, seaborn, matplotlib
Implementation of Digit recognizer using the Keras package with Tensorflow in the back end using Pyhton. Got an accuracy of 0.9816 in predicting digits from the images.
Repository containing codes made for different Kaggle's Competitions. Competitions in this repository: Digit Recognizer, Titanic and Kannada MNIST.
Contains basic model for digit recognition using CNN
MNIST handwritten digit recognizer built on PyTorch
Using a Convolutional Neural Network (CNN) to identify the Kannada numerical digits. Tensorflow (Keras) is used to create, train and load the neural network model. CustomTKinter/TKinter are used to provide the GUI and OpenCV is used to read input form the GUI.
This repository contains two sample implementations of the traditional Computer Vision task, Digit Recognizer from Kaggle. The URL of the dataset and relevant information can be found here: https://www.kaggle.com/c/digit-recognizer
This repository is for a digit-classifier-app that i'm building
MNIST Digit Recognizer with Machine Learning (CNN, MLP, KNN, SVM, Decision Tree, Random Forest)
The final experiment of machine learning 24, spring in HUST.
Numerical Methods: "Handwritten Digit Recognition" Group Project - 2nd Semester 2021 - Computer Science, UBA
Digit Recognizer is a Kaggle based competition. In this competition, goal is to correctly identify digits from a dataset of tens of thousands of handwritten images. Implemented different machine learning algorithms (Convolutional Neural Network, Random Forest, Support Vector Machine, Gradient Boosted Method) to improve the accuracy of model.
Mouse drawn live Digit Recognizer using Keras CNN and cv2 canvas
Digit Recognition with CNN
Hand Written Digit-recognizer build with Tensorflow and Numpy. The interface for this module is built with Gradio.
Digit Recognizer 91.4% accuracy
This is a neural network designed to train on the MNIST data set for recognizing handwritten digits.
This is a digit recogniser using Fastai ResNet.
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