View Code? Open in Web Editor
NEW
In this project we make use of convolutional neural network to recognise digits from 0 to 9. The neural network architecture used in this project is LENET-5.
digit---recognition's Introduction
The handwritten digit recognition can play a huge role in increasing the speed of the postal systems.
The letters can be segregated based on the PIN code using this model.
In this project we classify handwritten digits using deep learning.
The data used here is taken from Tensorflow datasets. To access the dataset click here.
DATA
SAMPLE SIZE
Train_data
60,000
Test_data
10,000
The image in this dataset are grayscale images. The dimension of each image is (28,28,3).
The height and width of the image is equal to 28.
Number of channels in the image is 1.
NEURAL_NETWORK ARCHITECTURE:
In this project we made use of the convolutional neural network.
The convolutional network architecture is LENET-5.
LENET-5 is one of the best architecture used for grayscale images.
In this project we are able to predict the handwritten digits with an accuracy of 99%.
digit---recognition's People
Contributors
Watchers
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent