Staff View: Offline yorùbá handwritten word recognition using geometric feature extraction and support vector machine classifier / Matthias Omotayo Oladele …[et al.]

Offline yorùbá handwritten word recognition using geometric feature extraction and support vector machine classifier / Matthias Omotayo Oladele …[et al.]

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùb...

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Main Authors:	Oladele, Matthias Omotayo, Adepoju, Temilola Morufat, Olatoke, Olaide Abiodun, Adewale Ojo, Oluwaseun
Format:	Article
Language:	English
Published:	Universiti Teknologi MARA 2020
Subjects:	Mathematical physics Elementary particle physics Extraction (Chemistry)
Online Access:	http://ir.uitm.edu.my/id/eprint/48113/1/48113.pdf http://ir.uitm.edu.my/id/eprint/48113/ https://mjoc.uitm.edu.my
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id	my.uitm.ir.48113
record_format	eprints
spelling	my.uitm.ir.481132021-06-24T07:36:12Z http://ir.uitm.edu.my/id/eprint/48113/ Offline yorùbá handwritten word recognition using geometric feature extraction and support vector machine classifier / Matthias Omotayo Oladele …[et al.] Oladele, Matthias Omotayo Adepoju, Temilola Morufat Olatoke, Olaide Abiodun Adewale Ojo, Oluwaseun Mathematical physics Elementary particle physics Extraction (Chemistry) Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features, Universiti Teknologi MARA 2020-10 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/48113/1/48113.pdf ID48113 Oladele, Matthias Omotayo and Adepoju, Temilola Morufat and Olatoke, Olaide Abiodun and Adewale Ojo, Oluwaseun (2020) Offline yorùbá handwritten word recognition using geometric feature extraction and support vector machine classifier / Matthias Omotayo Oladele …[et al.]. Malaysian Journal of Computing (MJoC), 5 (2). pp. 504-514. ISSN (eISSN): 2600-8238 https://mjoc.uitm.edu.my
institution	Universiti Teknologi Mara
building	Tun Abdul Razak Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Mara
content_source	UiTM Institutional Repository
url_provider	http://ir.uitm.edu.my/
language	English
topic	Mathematical physics Elementary particle physics Extraction (Chemistry)
spellingShingle	Mathematical physics Elementary particle physics Extraction (Chemistry) Oladele, Matthias Omotayo Adepoju, Temilola Morufat Olatoke, Olaide Abiodun Adewale Ojo, Oluwaseun Offline yorùbá handwritten word recognition using geometric feature extraction and support vector machine classifier / Matthias Omotayo Oladele …[et al.]
description	Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features,
format	Article
author	Oladele, Matthias Omotayo Adepoju, Temilola Morufat Olatoke, Olaide Abiodun Adewale Ojo, Oluwaseun
author_facet	Oladele, Matthias Omotayo Adepoju, Temilola Morufat Olatoke, Olaide Abiodun Adewale Ojo, Oluwaseun
author_sort	Oladele, Matthias Omotayo
title	Offline yorùbá handwritten word recognition using geometric feature extraction and support vector machine classifier / Matthias Omotayo Oladele …[et al.]
title_short	Offline yorùbá handwritten word recognition using geometric feature extraction and support vector machine classifier / Matthias Omotayo Oladele …[et al.]
title_full	Offline yorùbá handwritten word recognition using geometric feature extraction and support vector machine classifier / Matthias Omotayo Oladele …[et al.]
title_fullStr	Offline yorùbá handwritten word recognition using geometric feature extraction and support vector machine classifier / Matthias Omotayo Oladele …[et al.]
title_full_unstemmed	Offline yorùbá handwritten word recognition using geometric feature extraction and support vector machine classifier / Matthias Omotayo Oladele …[et al.]
title_sort	offline yorùbá handwritten word recognition using geometric feature extraction and support vector machine classifier / matthias omotayo oladele …[et al.]
publisher	Universiti Teknologi MARA
publishDate	2020
url	http://ir.uitm.edu.my/id/eprint/48113/1/48113.pdf http://ir.uitm.edu.my/id/eprint/48113/ https://mjoc.uitm.edu.my
_version_	1703963506984878080
score	13.211869

Offline yorùbá handwritten word recognition using geometric feature extraction and support vector machine classifier / Matthias Omotayo Oladele …[et al.]

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