scholarly journals A Survey on Handwritten Character Recognition using Machine Learning Technique

2021 ◽  
Vol 23 (06) ◽  
pp. 1019-1024
Author(s):  
Thatikonda Somashekar ◽  
Keyword(s):  
Machine Learning ◽  
Character Recognition ◽  
Machine Learning Technique ◽  
Handwritten Character Recognition ◽  
Comprehensive Overview ◽  
Processing Application ◽  
Handwritten Character ◽  
Learning Technique ◽  
Bank Checks ◽  
Machine Learning Tool

Due to its broad range of applications, handwritten character recognition is widespread. Processing application forms, digitizing ancient articles, processing postal addresses, processing bank checks, and many other handwritten character processing fields are increasing in popularity. Since the last three decades, handwritten characters have drawn the attention of researchers. For successful recognition, several methods have been suggested. This paper presents a comprehensive overview of handwritten character recognition using a neural network as a machine learning tool.

scholarly journals Handwritten Character Recognition using Deep Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 5815-5819
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Language Processing ◽  
Character Recognition ◽  
Human Life ◽  
General Purpose ◽  
Learning Problems ◽  
Handwritten Character Recognition ◽  
Handwritten Documents ◽  
Handwritten Character

In day to day human life, handwritten documents are a general purpose for communication and restoring their information. In the field of computer science, character recognition using Deep Learning has more attention. DL has a massive set of pattern recognition tools that can apply to speech recognition, image processing, natural language processing and has a remarkable capability to find out a solution for complex machine learning problems. DL can focus on the specific feature of an image to character recognition for enhancing efficiency and accuracy. In this paper, we have presented a methods for handwritten character recognition using deep learning.

An Empirical Comparison of Handwritten Character Recognition Using Machine Learning

2021 ◽  
Author(s):  
Smitha N ◽  
Rahul Kumar Singh ◽  
Subodh Kumar Yadav ◽  
Sandeep Sah ◽  
Keshava Kumar N ◽  
...  
Keyword(s):  
Machine Learning ◽  
Character Recognition ◽  
Empirical Comparison ◽  
Handwritten Character Recognition ◽  
Handwritten Character

scholarly journals Recognition of Off-line Kannada Handwritten Characters by Deep Learning using Capsule Network

2019 ◽  
Vol 8 (6) ◽  
pp. 4767-4778
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Character Recognition ◽  
State Of The Art ◽  
Learning Approaches ◽  
Deep Belief Networks ◽  
Problem Statement ◽  
Handwritten Character Recognition ◽  
Handwritten Character

Handwritten character recognition is an important subfield of Computer Vision which has the potential to bridge the gap between humans and machines. Machine learning and Deep learning approaches to the problem have yielded acceptable results throughout, yet there is still room for improvement. off-line Kannada handwritten character recognition is another problem statement in which many authors have shown interest, but the obtained results being acceptable. The initial efforts have used Gabor wavelets and moments functions for the characters. With the introduction of Machine Learning, SVMs and feature vectors have been tried to obtain acceptable accuracies. Deep Belief Networks, ANNs have also been used claiming a con- siderable increase in results. Further advanced techniques such as CNN have been reported to be used to recognize Kannada numerals only. In this work, we budge towards solving the problem statement with Capsule Networks which is now the state of the art technology in the field of Computer Vision. We also carefully consider the drawbacks of CNN and its impact on the problem statement, which are solved with the usage of Capsule Networks. Excellent results have been obtained in terms of accuracies. We take a step further to evaluate the technique in terms of specificity, precision and f1-score. The approach has performed extremely well in terms of these measures also

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