scholarly journals Research on Mnist Handwritten Numbers Recognition based on CNN

2021 ◽  
Vol 2138 (1) ◽  
pp. 012002
Author(s):  
Yang Gong ◽  
Pan Zhang
Keyword(s):  
Training Set ◽  
Accuracy Rate ◽  
Handwritten Digit Recognition ◽  
Data Set ◽  
Digit Recognition ◽  
Input Layer ◽  
Handwritten Digit ◽  
Model Training ◽  
Increasing Demand ◽  
Fully Connected

Abstract In view of the increasing demand for handwritten digit recognition, a handwritten digit recognition model based on convolutional neural network is proposed. The model includes 1 input layer and 2 convolutional layers (5*5 convolution Core), 2 pooling layers (2*2 pooling core), 1 fully connected layer, 1 output layer, and use the mnist data set for model training and prediction. After a lot of training and participation, the accuracy rate of the training set was finally reached to 100%, and the accuracy rate of 99.25% was also achieved on the test set, which can meet the requirements of recognizing handwritten digits.

Handwritten Digit Recognition Based on Modified LLE Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 2290-2293
Author(s):  
Bo Yu Gu ◽  
Ye Chun Li
Keyword(s):  
Euclidean Distance ◽  
Locally Linear Embedding ◽  
Learning Approach ◽  
Affine Transformations ◽  
Accuracy Rate ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Linear Embedding ◽  
Locally Linear

Locally linear embedding is an efficient manifold learning approach. A modified locally linear embedding algorithm is proposed to cope with the interferences of affine transformations in handwritten digit recognition. In order to offset all kinds of affine transformations, the Euclidean distance is replaced by the tangent distance which is more appropriate for handwritten digit recognition based on image. And the number of neighborhood is computed automatically based on the similarity of images. Experimental results show that the accuracy rate of is improved.

scholarly journals Handwritten Digit Recognition using Image Preprocessing and CNN

2020 ◽  
pp. 896-902
Author(s):  
Anshul Dubey ◽  
Ashley Lazarus ◽  
Dharmendra Mangal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Accuracy Rate ◽  
Handwritten Digit Recognition ◽  
Image Preprocessing ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Multilayer Architecture ◽  
Recognition Systems ◽  
Zip Code

Handwritten digit recognition, is a technique of identifying and enlisting the recognized digit, that uses neural networks, deep learning and machine learning. The applications and demand of handwritten digit recognition systems such as zip code recognition, car number plate recognition, robotics, banks, mobile applications and numerous more, are soaring every day. It can be done through numerous approaches, but convolutional neural network is considered one of the best methods. The special neural network uses multilayer architecture for identification and classification. Although the accuracy factor can be increased, based on image preprocessing, in this paper we discuss how the accuracy of the system can be increased for better handwritten digit recognition, using convolutional neural networks, image preprocessing; binarization, resizing, rotation. The accuracy rate obtained is 99.33%.

scholarly journals Handwritten Digit Recognition System

2021 ◽  
pp. 76-85
Author(s):  
Shubham Mendapara ◽  
Krish Pabani ◽  
Yash Paneliya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Strategies ◽  
Optical Character Recognition ◽  
Handwritten Digit Recognition ◽  
Neural Network Approach ◽  
Data Set ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Compositional Styles

Recently, handwritten digit recognition has become impressively significant with the escalation of the Artificial Neural Networks (ANN). Apart from this, deep learning has brought a major turnaround in machine learning, which was the main reason it attracted many researchers. We can use it in many applications. The main aim of this article is to use the neural network approach for recognizing handwritten digits. The Convolution Neural Network has become the center of all deep learning strategies. Optical character recognition (OCR) is a part of image processing that leads to excerpting text from images. Recognizing handwritten digits is part of OCR. Recognizing the numbers is an important and remarkable subject. In this way, since the handwritten digits are not of same size, thickness, position, various difficulties are faced in determining the problem of recognizing handwritten digits. The unlikeness and structure of the compositional styles of many entities further influences the example and presence of the numbers. This is the strategy for perceiving and organizing the written characters. Its applications are such as programmed bank checks, health, post offices, for education, etc. In this article, to evaluate CNN's performance, we used the MNIST dataset, which contains 60,000 images of handwritten digits. Achieves 98.85% accuracy for handwritten digit. And where 10% of the total images were used to test the data set.

Sign in / Sign up

Export Citation Format

Share Document