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Detection of handwritten text using convolutional neural network

bracu.degree.levelUndergraduate
bracu.type.groupStudent Works
datacite.rightsOpen Access
dc.contributor.advisorUddin, Jia
dc.contributor.authorJasim, Rabib Bin
dc.contributor.authorMahin, Rokeya Sultana
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2024-06-03T06:24:53Z
dc.date.available2024-06-03T06:24:53Z
dc.date.copyright©2019
dc.date.issued2019-04
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (page 23).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.en_US
dc.description.abstractMachine replication of human functions, like reading, is an ancient dream. However, over the last five decades, machine reading has grown from a dream to reality. We have tried to make it more obvious through a hand writing recognition system. This research paper describes a text-line extraction based method. It offers a new solution to traditional handwriting recognition techniques using concepts of Deep learning and computer vision. An image can have hand writing, typed letters, different characters and other images. Our intention is to detect all the characters and display them. Some images can also have unnecessary lines or unclear letters. This system will clear the picture through pre-processing system and will be able to identify the letters or characters. It will help people to identify any unclear messages. It will also avoid unnecessary images and will focus on the text only. Sometimes we want to ignore unnecessary advertisement images from the newspapers. Our system will do a great work for this. It will clear all the images and unnecessary lines etc. and will only display the text what people want to read.en_US
dc.description.degreeBachelor of Science in Computer Science
dc.description.statementofresponsibilityRabib Bin Jasim
dc.description.statementofresponsibilityRokeya Sultana Mahin
dc.format.extent35 pages
dc.identifier.otherID 12221015
dc.identifier.otherID 15101135
dc.identifier.urihttp://hdl.handle.net/10361/23084
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.subjectConvolutional neural networken_US
dc.subjectMachine learningen_US
dc.subjectText-line extractionen_US
dc.subjectDeep learningen_US
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshData mining
dc.titleDetection of handwritten text using convolutional neural networken_US
dc.typeThesisen_US

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