YORÙBÁ CHARACTER RECOGNITION SYSTEM USING CONVOLUTIONAL RECURRENT NEURAL NETWORK
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Date
2022-09-01
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Ugur SEN
Abstract
Abstract: Handwritten recognition systems enable automatic recognition of human handwritings, thereby increasing humancomputer interaction. Despite enormous efforts in handwritten recognition, little progress has been made due to the variability of
human handwriting, which presents numerous difficulties for machines to recognize. It was discovered that while tremendous
progress has been made in handwritten recognition of English and Arabic languages, very little work has been done on Yorùbá
handwritten characters. Those few works, in turn, made use of Hidden Markov Model (HMM), Support Vector Machine (SVM), Bayes
theorem, and decision tree algorithms. To integrate and save one of Nigeria's indigenous languages from extinction, as well as to make
Yorùbá documents accessible and available in the digital world, this research work was undertaken. The research presents a
convolutional recurrent neural network (CRNN) for the recognition of Yorùbá handwritten characters. Data were collected from
students of Kwara State University who were literate Yorùbá writers. The collected data were subjected to some level of preprocessing
such as grayscale, binarization, and normalization in order to remove perturbations introduced during the digitization process. The
convolutional recurrent neural network model was trained using the preprocessed images. The evaluation was conducted using the
acquired Yorùbá characters, 87.5% of the acquired images were used for the training while 12.5% were used to evaluate the developed
system. As there is currently no publicly available database of Yorùbá characters for validating Yorùbá recognition systems. The
resulting recognition accuracy was 87.2% while the characters with under dot and diacritic signs has low recognition accuracy
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Ajao JF, Yusuff SR, Ajao AO. 2022. Yorùbá character recognition system using convolutional recurrent neural network. BSJ Eng Sci, 5(4): 151-157