Numerous works have been proposed
and implemented in computerization of various
human languages, nevertheless, miniscule effort
have also been made so as to put Yorùbá
Handwritten Character on the map of Optical
Character Recognition. This study presents a novel
technique in the development of Yorùbá alphabets
recognition system through the use of deep
learning. The developed model was implemented
on Matlab R2018a environment using the
developed framework where 10,500 samples of
dataset were for training and 2100 samples were
used for testing. The training of the developed
model was conducted using 30 Epoch, at 164
iteration per epoch while the total iteration is 4920
iterations. Also, the training period was estimated
to 11296 minutes 41 seconds. The model yielded the
network accuracy of 100% while the accuracy of
the test set is 97.97%, with F1 score of 0.9800,
Precision of 0.9803 and Recall value of 0.9797.
Incorporating Planning Intelligence into Deep Learning: A Planning Support Tool for Street Network Design
