scholarly journals Tablet identification using support vector machine based text recognition and error correction by enhanced n-grams algorithm

2020 ◽  
Vol 14 (7) ◽  
pp. 1366-1372
Author(s):  
A.B. Dhivya ◽  
M. Sundaresan
Keyword(s):  
Support Vector Machine ◽  
Error Correction ◽  
Text Recognition ◽  
Support Vector

Automatic EEG Artifact Removal: A Weighted Support Vector Machine Approach With Error Correction

2009 ◽  
Vol 56 (2) ◽  
pp. 336-344 ◽  
Author(s):  
Shi-Yun Shao ◽  
Kai-Quan Shen ◽  
Chong Jin Ong ◽  
E. Wilder-Smith ◽  
Xiao-Ping Li
Keyword(s):  
Support Vector Machine ◽  
Error Correction ◽  
Support Vector ◽  
Artifact Removal

scholarly journals Multi-class Classification Method of Support Vector Machine Based on Error Correction Coding

2021 ◽  
Author(s):  
Li Junfei ◽  
Zhao Longhai
Keyword(s):  
Support Vector Machine ◽  
Error Correction ◽  
Classification Method ◽  
Radiation Environment ◽  
Support Vector ◽  
Single Event ◽  
Error Correction Coding ◽  
Multi Class Classification ◽  
Multi Classification ◽  
Space Radiation Environment

Abstract In the space radiation environment, there will be many errors in the multi-classification results of support vector machine which caused by single event flipping , the ability of correcting classification errors through error correction coding is studied in this paper, results of simulation confirm that error correction coding can increase the accuracy ,which is beneficial for anti-single event flip.

scholarly journals Handwritten Digit and Text Recognition Based On Convolutional Neural Network Approach

2021 ◽  
Vol 23 (07) ◽  
pp. 1518-1525
Author(s):  
◽  
Vivekanandan S J ◽  
Dr Sivasubramanian S ◽  
◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Text Recognition ◽  
Support Vector ◽  
Network Approach ◽  
Neural Network Approach ◽  
Handwritten Digit

The goal of this undertaking is to foster a powerful penmanship acknowledgment methods utilizing ideas of Machine learning and PC vision. An expansion of MNIST digits dataset called the Emnist dataset has been utilized. It contains 62 classes with 0-9 digits and A-Z characters in both capitalized and lowercase. To recognize transcribed content and convert it into computerized structure utilizing Convolutional Neural Network and Support Vector Machine, shortened as CNN and SVM, for text arrangement and identification, has been made. Before that we pre-prepared the dataset and applied different channels over it. Our framework will perceive the content precisely utilizing tensorflow libraries.

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