Multi‐Lingual Handwritten Character Recognition Using Deep Learning

2021 ◽  
pp. 155-180
Author(s):  
Giriraj Parihar ◽  
Ratnavel Rajalakshmi ◽  
J. Bhuvana
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Handwritten Character Recognition ◽  
Handwritten Character

scholarly journals Devanagari Handwritten Character Recognition Using Deep Learning

2022 ◽  
Vol 10 (1) ◽  
pp. 102-105
Author(s):  
Kannuru Padmaja
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Automation System ◽  
Handwritten Character Recognition ◽  
Image Processing Techniques ◽  
Handwritten Character ◽  
Processing Techniques ◽  
Deep Learning Model

Abstract: In this paper, we present the implementation of Devanagari handwritten character recognition using deep learning. Hand written character recognition gaining more importance due to its major contribution in automation system. Devanagari script is one of various languages script in India. It consists of 12 vowels and 36 consonants. Here we implemented the deep learning model to recognize the characters. The character recognition mainly five steps: pre-processing, segmentation, feature extraction, prediction, post-processing. The model will use convolutional neural network to train the model and image processing techniques to use the character recognition and predict the accuracy of rcognition. Keywords: convolutional neural network, character recognition, Devanagari script, deep learning.

scholarly journals Deep Learning for Roman Handwritten Character Recognition

2018 ◽  
Vol 12 (2) ◽  
pp. 455 ◽  
Author(s):  
Muhaafidz Md Saufi ◽  
Mohd Afiq Zamanhuri ◽  
Norasiah Mohammad ◽  
Zaidah Ibrahim
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Character Recognition ◽  
Accurate Result ◽  
Big Data Analysis ◽  
Learning Method ◽  
Learning Models ◽  
Handwritten Character Recognition ◽  
Handwritten Character

The advantage of deep learning is that the analysis and learning of massive amounts of unsupervised data make it a beneficial tool for Big Data analysis. Convolution Neural Network (CNN) is a deep learning method that can be used to classify image, cluster them by similarity, and perform image recognition in the scene. This paper conducts a comparative study between three deep learning models, which are simple-CNN, AlexNet and GoogLeNet for Roman handwritten character recognition using Chars74K dataset. The produced results indicate that GooleNet achieves the best accuracy but it requires a longer time to achieve such result while AlexNet produces less accurate result but at a faster rate.

Automated Handwritten Character Recognition using Deep Learning

2019 ◽  
Vol 51 (03) ◽  
pp. 477--482
Author(s):  
M. HUMAYUN ◽  
R. SIDDIQUI ◽  
S. S. ZIA ◽  
M. NASEEM ◽  
I. MALA ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Handwritten Character Recognition ◽  
Handwritten Character
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