Detection of handwritten text using convolutional neural network
Date
2019-04
Journal Title
Journal ISSN
Volume Title
Publisher
BRAC University
Abstract
Machine 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.
Description
Cataloged from PDF version of thesis.
Includes bibliographical references (page 23).
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.
Includes bibliographical references (page 23).
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.
Keywords
Convolutional neural network, Machine learning, Text-line extraction, Deep learning
