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dc.contributor.advisorUddin, Jia
dc.contributor.authorAl Emran, Md.
dc.contributor.authorNaief, S. M.
dc.contributor.authorHossain, Md. Shimul
dc.date.accessioned2018-12-03T09:16:47Z
dc.date.available2018-12-03T09:16:47Z
dc.date.copyright2018
dc.date.issued2018
dc.identifier.otherID 14301030
dc.identifier.otherID 14101122
dc.identifier.otherID 14301027
dc.identifier.urihttp://hdl.handle.net/10361/10950
dc.descriptionThis thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 30-32).
dc.description.abstractHandwritten 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.en_US
dc.description.statementofresponsibilityMd. Al Emran
dc.description.statementofresponsibilityS. M. Naief
dc.description.statementofresponsibilityMd. Shimul Hossain
dc.format.extent32 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC 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.
dc.subjectSVMen_US
dc.subjectRFCen_US
dc.subjectKNNen_US
dc.subjectPCAen_US
dc.subjectmRMRen_US
dc.subjectHandwriting recognitionen_US
dc.subjectChain Codeen_US
dc.subjectDimension reductionen_US
dc.subject.lcshOptical character recognition devices
dc.subject.lcshWriting -- Identification -- Data processing
dc.subject.lcshData Mining.
dc.subject.lcshComputer algorithms
dc.subject.lcshMachine learning
dc.titleHandwritten character recognition and prediction of age, gender and handedness using machine learningen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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