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dc.contributor.advisorChakrabarty, Amitabha
dc.contributor.advisorIslam, Md Saiful
dc.contributor.authorMahal, Somania Nur
dc.contributor.authorAbir, B M
dc.contributor.authorBakhtiar, Fahim
dc.date.accessioned2018-01-11T09:56:17Z
dc.date.available2018-01-11T09:56:17Z
dc.date.copyright2017
dc.date.issued2017-08-21
dc.identifier.otherID 13301124
dc.identifier.otherID 12201022
dc.identifier.otherID 16341028
dc.identifier.urihttp://hdl.handle.net/10361/9032
dc.descriptionThis thesis report is submitted in partial fulfilment 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 (pages 27-28).
dc.description.abstractHandwritten text detection from a natural image has a large set of difficulties. A systematic approach that can automatically recognise text from handwriting, printed books, road signs and also classifies text and nontext blocks from natural image has many significant applications. For instance, visual assistance for visually impaired people, image understanding, classification of text in image, implementing autonomous navigation system. Recent development of deep learning approach has strong capabilities to extract high level feature from a kernel(patch) of an Image. In this thesis we will demonstrate an alternate approach that integrates a multilayer convolutional neural network (CNN) with supervised feature learning .This approach allows a higher recall rate for the text in an image and thus increases the overall performances of the system. And we have used these methodologies to create a learning model using synthetic and real-world data that is capable to process bangla and english handwritten and scene text in natural image.en_US
dc.description.statementofresponsibilitySomania Nur Mahal
dc.description.statementofresponsibilityB M Abir
dc.description.statementofresponsibilityFahim Bakhtiar
dc.format.extent28 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.subjectText detectionen_US
dc.subjectNeural networken_US
dc.subjectReal-world dataen_US
dc.subjectNatural imageen_US
dc.subjectNontext blocksen_US
dc.titleEnd to end Bangla handwritten and scene text detection 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


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