Handwritten character recognition and prediction of age, gender and handedness using machine learning

This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.

Detalhes bibliográficos
Principais autores:	Al Emran, Md., Naief, S. M., Hossain, Md. Shimul
Outros Autores:	Uddin, Jia
Formato:	Tese
Idioma:	English
Publicado em:	BRAC University 2018
Assuntos:	SVM RFC KNN PCA mRMR Handwriting recognition Chain Code Dimension reduction Optical character recognition devices Writing > Identification > Data processing Data Mining. Computer algorithms Machine learning
Acesso em linha:	http://hdl.handle.net/10361/10950

id	10361-10950
record_format	dspace
spelling	10361-109502022-01-26T10:13:16Z Handwritten character recognition and prediction of age, gender and handedness using machine learning Al Emran, Md. Naief, S. M. Hossain, Md. Shimul Uddin, Jia Department of Computer Science and Engineering, BRAC University SVM RFC KNN PCA mRMR Handwriting recognition Chain Code Dimension reduction Optical character recognition devices Writing -- Identification -- Data processing Data Mining. Computer algorithms Machine learning This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 30-32). Handwritten character recognition and prediction of age, gender & handedness from handwritten documents offers an interesting research problem for researchers as few research carried out on this field. The aim of this research is to investigate machine learning classification algorithm that is used to recognize different writer’s attributes and their handwritten characters. Predicting writer’s identity and recognizing handwritten characters based on mainly three steps: segmentation, feature extraction and classification. In the segmentation step we used edge detection technique for segmenting dataset images using fuzzy logic. Feature extraction methods are described to take decision category of our writers and their handwritings. For feature extraction we used mRMR for feature selection, tortuosity, direction, curvatures and chain code for feature extraction and PCA for dimension reduction. In the final step, we used KNN, SVM and RFC for classification of writer attributes and recognizing handwritten characters. Classification accuracy on QUWI dataset were 89.41% for recognizing handwritten character, 88.28% for age range prediction, 75.90% for gender prediction and 75.11% for handedness prediction for each writer. We have used these classification algorithms to bring out the maximum accuracy rate for predicting age, gender & handedness. Md. Al Emran S. M. Naief Md. Shimul Hossain B. Computer Science and Engineering 2018-12-03T09:16:47Z 2018-12-03T09:16:47Z 2018 2018 Thesis ID 14301030 ID 14101122 ID 14301027 http://hdl.handle.net/10361/10950 en BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 32 pages application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	English
topic	SVM RFC KNN PCA mRMR Handwriting recognition Chain Code Dimension reduction Optical character recognition devices Writing -- Identification -- Data processing Data Mining. Computer algorithms Machine learning
spellingShingle	SVM RFC KNN PCA mRMR Handwriting recognition Chain Code Dimension reduction Optical character recognition devices Writing -- Identification -- Data processing Data Mining. Computer algorithms Machine learning Al Emran, Md. Naief, S. M. Hossain, Md. Shimul Handwritten character recognition and prediction of age, gender and handedness using machine learning
description	This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
author2	Uddin, Jia
author_facet	Uddin, Jia Al Emran, Md. Naief, S. M. Hossain, Md. Shimul
format	Thesis
author	Al Emran, Md. Naief, S. M. Hossain, Md. Shimul
author_sort	Al Emran, Md.
title	Handwritten character recognition and prediction of age, gender and handedness using machine learning
title_short	Handwritten character recognition and prediction of age, gender and handedness using machine learning
title_full	Handwritten character recognition and prediction of age, gender and handedness using machine learning
title_fullStr	Handwritten character recognition and prediction of age, gender and handedness using machine learning
title_full_unstemmed	Handwritten character recognition and prediction of age, gender and handedness using machine learning
title_sort	handwritten character recognition and prediction of age, gender and handedness using machine learning
publisher	BRAC University
publishDate	2018
url	http://hdl.handle.net/10361/10950
work_keys_str_mv	AT alemranmd handwrittencharacterrecognitionandpredictionofagegenderandhandednessusingmachinelearning AT naiefsm handwrittencharacterrecognitionandpredictionofagegenderandhandednessusingmachinelearning AT hossainmdshimul handwrittencharacterrecognitionandpredictionofagegenderandhandednessusingmachinelearning
_version_	1814308055862476800

Handwritten character recognition and prediction of age, gender and handedness using machine learning

Registros relacionados