Abstract:
Character recognition has been an important
area of research in the last few decades. It is basically divided
into two major types namely online and offline (handwritten)
character recognitions. Characters with tonal marks (diacritics)
such as Yorùbá characters (orthography) had posed more
challenges than their counterparts with no tonal marks and as a
result require some optimization methods to improve the
recognition rate and reduce the error rate. This study evaluated
the performance of four optimized backpropagation algorithms,
Levenberg-Marquardt, Quasi-Newton BFGS, Resilient
Propagation and Scaled Conjugate Gradient, on Yorùbá
character recognition. The method used in this study involves the
five basic stages of image processing namely; image acquisition,
image preprocessing, segmentation, feature extraction and
classification. The performances of the algorithms were
experimentally measured using mean squared error (MSE),
epochs, accuracy and response time. From the experiments, it
was observed that the Levenberg-Marquardt training algorithm
has the best accuracy of 98.8%; Resilient Propagation and Scaled
Conjugate Gradient are the fastest to converge with an average
response time of 2 seconds. The results obtained can serve as a
fundamental guideline in adopting the most relevant training
algorithm for character image recognition.
