End to end Bangla handwritten and scene text detection using convolutional neural network

Cataloged from PDF version of thesis report.

Bibliografiske detaljer
Main Authors: Mahal, Somania Nur, Abir, B M, Bakhtiar, Fahim
Andre forfattere: Chakrabarty, Amitabha
Format: Thesis
Sprog:English
Udgivet: BRAC University 2018
Fag:
Online adgang:http://hdl.handle.net/10361/9032
id 10361-9032
record_format dspace
spelling 10361-90322022-01-26T10:18:13Z End to end Bangla handwritten and scene text detection using convolutional neural network Mahal, Somania Nur Abir, B M Bakhtiar, Fahim Chakrabarty, Amitabha Islam, Md Saiful Department of Computer Science and Engineering, BRAC University Text detection Neural network Real-world data Natural image Nontext blocks Cataloged from PDF version of thesis report. Includes bibliographical references (pages 27-28). This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017. Handwritten 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. Somania Nur Mahal B M Abir Fahim Bakhtiar B. Computer Science and Engineering 2018-01-11T09:56:17Z 2018-01-11T09:56:17Z 2017 8/21/2017 Thesis ID 13301124 ID 12201022 ID 16341028 http://hdl.handle.net/10361/9032 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. 28 pages application/pdf BRAC University
institution Brac University
collection Institutional Repository
language English
topic Text detection
Neural network
Real-world data
Natural image
Nontext blocks
spellingShingle Text detection
Neural network
Real-world data
Natural image
Nontext blocks
Mahal, Somania Nur
Abir, B M
Bakhtiar, Fahim
End to end Bangla handwritten and scene text detection using convolutional neural network
description Cataloged from PDF version of thesis report.
author2 Chakrabarty, Amitabha
author_facet Chakrabarty, Amitabha
Mahal, Somania Nur
Abir, B M
Bakhtiar, Fahim
format Thesis
author Mahal, Somania Nur
Abir, B M
Bakhtiar, Fahim
author_sort Mahal, Somania Nur
title End to end Bangla handwritten and scene text detection using convolutional neural network
title_short End to end Bangla handwritten and scene text detection using convolutional neural network
title_full End to end Bangla handwritten and scene text detection using convolutional neural network
title_fullStr End to end Bangla handwritten and scene text detection using convolutional neural network
title_full_unstemmed End to end Bangla handwritten and scene text detection using convolutional neural network
title_sort end to end bangla handwritten and scene text detection using convolutional neural network
publisher BRAC University
publishDate 2018
url http://hdl.handle.net/10361/9032
work_keys_str_mv AT mahalsomanianur endtoendbanglahandwrittenandscenetextdetectionusingconvolutionalneuralnetwork
AT abirbm endtoendbanglahandwrittenandscenetextdetectionusingconvolutionalneuralnetwork
AT bakhtiarfahim endtoendbanglahandwrittenandscenetextdetectionusingconvolutionalneuralnetwork
_version_ 1814308669819453440