Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz

Today, handwritten recognition becomes a very crucial area in the field of pattern recognition and image processing. Deep learning was commonly used for handwriting recognition. Recognition of handwritten text is analyzed in offline handwriting. The only information that can be analyzed is a charact...

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Main Author:	Abd Aziz, Nur Hasyimah
Format:	Thesis
Language:	English
Published:	2020
Subjects:	Instruments and machines Online data processing Neural networks (Computer science) Analytic mechanics
Online Access:	https://ir.uitm.edu.my/id/eprint/55334/1/55334.pdf
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spelling	my-uitm-ir.553342022-01-26T03:19:55Z Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz 2020-07 Abd Aziz, Nur Hasyimah Instruments and machines Electronic Computers. Computer Science Online data processing Neural networks (Computer science) Web-based user interfaces. User interfaces (Computer systems) Analytic mechanics Today, handwritten recognition becomes a very crucial area in the field of pattern recognition and image processing. Deep learning was commonly used for handwriting recognition. Recognition of handwritten text is analyzed in offline handwriting. The only information that can be analyzed is a character's binary production against a context. While shifting to digital writing stylus gives more detail, such as pen movement, pressure and writing speed, there is still a need for offline methods when it is unavailable online. Postal address, historical documents, archives, or mass digitization of hand-filled forms are especially needed. Extensive work in this area has led to considerable change from conventional methods to human-competitive performance. The conversion of handwritten image into digital format required more time and often affected by errors. Next, noise has been recognized as one of the major issues that decrease the performance of handwritten recognition system. Therefore, this study will develop handwritten recognition system by using Convolutional Neural Network (CNN) as a classifier. CNN was chosen as an algorithm for classification task because various studies had concluded that it is able to produce highly accurate result. The proposed system is able to perform the conversion of handwritten image into digital format that are executable by computer. The CNN model was trained and tested by the dataset that obtain from Kaggle website where it was derived from EMNIST database. Several experiments were done in order to obtain higher accuracy result. In order to evaluate the model, we calculate the accuracy. Accuracy is fraction of labels that the network predicts correctly. After experiment complete, the accuracy achieved from the project is 99.56%. This result will prove that CNN can be the great classifier as it can produce high accuracy rate. Next, the system was successfully developed by implementing the best CNN model as a classifier. The system is able to input an image that contain handwritten text, preprocess the image, recognize the text and finally produce an output which is editable text. Functionality test was done in order to evaluate the system. The result of the functionality test is most of the user are satisfy with the system. The future work may be developed by finding the new way in order the computer recognize the text without segment the image into separated character. 2020-07 Thesis https://ir.uitm.edu.my/id/eprint/55334/ https://ir.uitm.edu.my/id/eprint/55334/1/55334.pdf text en public degree Universiti Teknologi MARA, Terengganu Faculty of Computer and Mathematical Sciences Jantan, Hamidah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Jantan, Hamidah
topic	Instruments and machines Instruments and machines Online data processing Neural networks (Computer science) Instruments and machines Analytic mechanics
spellingShingle	Instruments and machines Instruments and machines Online data processing Neural networks (Computer science) Instruments and machines Analytic mechanics Abd Aziz, Nur Hasyimah Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz
description	Today, handwritten recognition becomes a very crucial area in the field of pattern recognition and image processing. Deep learning was commonly used for handwriting recognition. Recognition of handwritten text is analyzed in offline handwriting. The only information that can be analyzed is a character's binary production against a context. While shifting to digital writing stylus gives more detail, such as pen movement, pressure and writing speed, there is still a need for offline methods when it is unavailable online. Postal address, historical documents, archives, or mass digitization of hand-filled forms are especially needed. Extensive work in this area has led to considerable change from conventional methods to human-competitive performance. The conversion of handwritten image into digital format required more time and often affected by errors. Next, noise has been recognized as one of the major issues that decrease the performance of handwritten recognition system. Therefore, this study will develop handwritten recognition system by using Convolutional Neural Network (CNN) as a classifier. CNN was chosen as an algorithm for classification task because various studies had concluded that it is able to produce highly accurate result. The proposed system is able to perform the conversion of handwritten image into digital format that are executable by computer. The CNN model was trained and tested by the dataset that obtain from Kaggle website where it was derived from EMNIST database. Several experiments were done in order to obtain higher accuracy result. In order to evaluate the model, we calculate the accuracy. Accuracy is fraction of labels that the network predicts correctly. After experiment complete, the accuracy achieved from the project is 99.56%. This result will prove that CNN can be the great classifier as it can produce high accuracy rate. Next, the system was successfully developed by implementing the best CNN model as a classifier. The system is able to input an image that contain handwritten text, preprocess the image, recognize the text and finally produce an output which is editable text. Functionality test was done in order to evaluate the system. The result of the functionality test is most of the user are satisfy with the system. The future work may be developed by finding the new way in order the computer recognize the text without segment the image into separated character.
format	Thesis
qualification_level	Bachelor degree
author	Abd Aziz, Nur Hasyimah
author_facet	Abd Aziz, Nur Hasyimah
author_sort	Abd Aziz, Nur Hasyimah
title	Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz
title_short	Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz
title_full	Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz
title_fullStr	Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz
title_full_unstemmed	Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz
title_sort	postal address handwritten recognition using convolutional neural network / nur hasyimah abd aziz
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2020
url	https://ir.uitm.edu.my/id/eprint/55334/1/55334.pdf
_version_	1783734913654063104

Postal address handwritten recognition using convolutional neural network / Nur Hasyimah Abd Aziz

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