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 A Supervised Machine Learning Approach for Post-OCR Error Detection for Historical Text

2021 ◽  
Author(s):  
Dana Dannélls ◽  
Shafqat Virk
Keyword(s):  
Machine Learning ◽  
Error Detection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
N Gram ◽  
Traditional Approaches

Training machine learning models with high accuracy requires careful feature engineering, which involves finding the best feature combinations and extracting their values from the data. The task becomes extremely laborious for specific problems such as post Optical Character Recognition (OCR) error detection because of the diversity of errors in the data. In this paper we present a machine learning approach which exploits character n-gram statistics as the only feature for the OCR error detection task. Our method achieves a significant improvement over the baseline reaching state-of-the-art results of 91% and 89% F1 score on English and Swedish datasets respectively. We report various experiments to select the appropriate machine learning algorithm and to compare our approach to previously reported traditional approaches.

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.

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