Show simple item record

dc.contributor.advisorMostakim, Moin
dc.contributor.authorAlif, Mujadded Al Rabbani
dc.contributor.authorAhmed, Sabbir
dc.contributor.authorAninda, Aleo
dc.contributor.authorDas, Tanoy Kumar
dc.date.accessioned2018-01-02T09:30:18Z
dc.date.available2018-01-02T09:30:18Z
dc.date.copyright2017
dc.date.issued2017
dc.identifier.otherID 13301066
dc.identifier.otherID 13301109
dc.identifier.otherID 13301113
dc.identifier.otherID 13301123
dc.identifier.urihttp://hdl.handle.net/10361/8870
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 36-36).
dc.description.abstractThis paper proposes a mechanism of Handwritten Letter and Digit Recognition (HLDR) to decipher images of Bangla handwritten characters into electronically editable format, which holds an important role in augmenting and digitalizing many analog application, which will not only paves the way to further research but also have many practical applications in current times.The mechanisms of HLDR has been studied broadly in the last half century, moreover, the rapid growth of computational power and main memory breaks the barrier and gives the opportunity for the implementation of more efficient and complex HLDR methodologies, which creates an increasing demand on many forthcoming application domains. In the field of pattern recognition one of the most productive way of achieving higher accuracy or lower error rate is to adopt an architecture that is deep, optimized and can process a large number of data. Therefore, this paper propose that using deeper residual network [1](ResNet) architecture and recently released Bangla-lekha dataset [2], we can achieve a result which is higher than any research that has been done before.en_US
dc.description.statementofresponsibilityMujadded Al Rabbani Alif
dc.description.statementofresponsibilitySabbir Ahmed
dc.description.statementofresponsibilityAleo Aninda
dc.description.statementofresponsibilityTanoy Kumar Das
dc.format.extent36 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis 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.subjectNeural networken_US
dc.subjectHandwritten characteren_US
dc.subjectDigit recognitionen_US
dc.titleIsolated bangla handwritten character & digit recognition using convolutional neural networken_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering  


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record