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

Ajao, Jumoke Falilat; Olawuyi, David Olufemi; Odejobi, Odetunji Ode

doi:10.14710/jtsiskom.6.4.2018.129-134

Cited by 13 publications

(17 citation statements)

References 10 publications

(17 reference statements)

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“…Oleh karena itu, teknik biometrik ini semakin mendapat perhatian untuk diimplementasikan di sistem keamanan [9]. Contoh implementasi dari metode biometrik adalah dengan sidik jari [10], iris mata [11], DNA [12], gestur tangan [13], tulisan tangan [14], dan pengenalan wajah [15]. Dari beberapa sistem biometrik tersebut, yang paling sedikit interaksi untuk identifikasi adalah metode pengenalan wajah [16].…”

Section: Pendahuluanunclassified

Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi

Subiyanto

Priliyana

Riyadani

et al. 2020

Jurnal Teknologi dan Sistem Komputer

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Genetic algorithm (GA) can improve the classification of the face recognition process in the principal component analysis (PCA). However, the accuracy of this algorithm for the smart home security system has not been further analyzed. This paper presents the accuracy of face recognition using PCA-GA for the smart home security system on Raspberry Pi. PCA was used as the face recognition algorithm, while GA to improve the classification performance of face image search. The PCA-GA algorithm was implemented on the Raspberry Pi. If an authorized person accesses the door of the house, the relay circuit will unlock the door. The accuracy of the system was compared to other face recognition algorithms, namely LBPH-GA and PCA. The results show that PCA-GA face recognition has an accuracy of 90 %, while PCA and LBPH-GA have 80 % and 90 %, respectively.

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Section: Pendahuluanunclassified

Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi

Subiyanto

Priliyana

Riyadani

et al. 2020

Jurnal Teknologi dan Sistem Komputer

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“…Ajao dkk. [8] juga berhasil menggunakan KNN untuk klasifikasi aksara Yoruba. KNN juga dapat dipergunakan untuk klasifikasi data video, seperti yang digunakan oleh Alasady dan Al-amery [9] dengan akurasi di atas 75 %.…”

Section: Pendahuluanunclassified

“…Unjuk kerja KNN untuk klasifikasi juga disimpulkan lebih baik jika dibandingkan dengan metode SVM dalam [8], [9] dan dengan Bayes dalam [8]. KNN sebagai metode klasifikasi juga dinilai lebih efisien daripada metode Bayes dalam [2], [10].…”

Section: Pendahuluanunclassified

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K-nearest neighbor performance for Nusantara scripts image transliteration

Widiarti

2020

Jurnal Teknologi dan Sistem Komputer

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The concept of classification using the k-nearest neighbor (KNN) method is simple, easy to understand, and easy to be implemented in the system. The main challenge in classification with KNN is determining the proximity measure of an object and how to make a compact reference class. This paper studied the implementation of the KNN for the automatic transliteration of Javanese, Sundanese, and Bataknese script images into Roman script. The study used the KNN algorithm with the number k set to 1, 3, 5, 7, and 9. Tests used the image dataset of 2520 data. With the 3-fold and 10-fold cross-validation, the results exposed the accuracy differences if the area of the extracted image, the number of neighbors in the classification, and the number of data training were different.

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“…The system recognizes only upper-case characters and achieved a recognition rate of 76.7% and a rejection rate of 23.3%. Ajao et al (2018) developed an offline Yorùbá character recognition system using Freeman chain code and K-Nearest Neighbor (KNN). A recognition accuracy of 87.7% was obtained from the KNN classification algorithm and the Freeman chain code.…”

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confidence: 99%

Offline Yorùbá Handwritten Word Recognition Using Geometric Feature Extraction and Support Vector Machine Classifier

Oladele¹,

Adepoju²,

Olatoke³

et al. 2020

MJoC

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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.

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

Cited by 13 publications

References 10 publications

Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi

Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi

K-nearest neighbor performance for Nusantara scripts image transliteration

Offline Yorùbá Handwritten Word Recognition Using Geometric Feature Extraction and Support Vector Machine Classifier

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