Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier

Jumoke Falilat Ajao  -  Department of Computer Science, Kwara State University, Nigeria
*David Olufemi Olawuyi  -  Department of Computer Science, Kwara State University, Nigeria
Odetunji Ode Odejobi  -  Department of Computer Engineering, Obafemi Awolowo University, Nigeria
Received: 23 Mar 2018; Accepted: 21 Apr 2018; Published: 31 Oct 2018.
Open Access Copyright (c) 2018 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Article Info
Section: Original Research Articles
Language: EN
Statistics: 1042 558
This work presents a recognition system for Offline Yoruba characters recognition using Freeman chain code and K-Nearest Neighbor (KNN). Most of the Latin word recognition and character recognition have used k-nearest neighbor classifier and other classification algorithms. Research tends to explore the same recognition capability on Yoruba characters recognition. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing to enhance the quality of the digitized images. Freeman chain code was used to extract the features of THE digitized images and KNN was used to classify the characters based on feature space. The performance of the KNN was compared with other classification algorithms that used Support Vector Machine (SVM) and Bayes classifier for recognition of Yoruba characters. It was observed that the recognition accuracy of the KNN classification algorithm and the Freeman chain code is 87.7%, which outperformed other classifiers used on Yoruba characters.
Keywords: Yoruba characters recognition; Freeman chain code; K-Nearest Neighbor classification

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