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

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


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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,


Item Type: Article
Oladele, Matthias Omotayo
Adepoju, Temilola Morufat
Olatoke, Olaide Abiodun
Adewale Ojo, Oluwaseun
Subjects: Q Science > QC Physics > Mathematical physics
Q Science > QC Physics > Nuclear and particle physics. Atomic energy. Radioactivity > Elementary particle physics
Q Science > QD Chemistry > Extraction (Chemistry)
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences
Journal or Publication Title: Malaysian Journal of Computing (MJoC)
UiTM Journal Collections: UiTM Journal > Malaysian Journal of Computing (MJoC)
ISSN: (eISSN): 2600-8238
Volume: 5
Number: 2
Page Range: pp. 504-514
Official URL:
Item ID: 48113
Uncontrolled Keywords: Gradient, Handwritten, Offline

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