Combination of Multiple Classifiers for Off-Line
Handwritten Arabic Word Recognition
laboratory of Science and Information Technologies and Communication,
University of 08 may 1945, Algeria
Abstract: This study investigates the
combination of different classifiers to improve Arabic handwritten word
recognition. Features based on Discrete Cosine Transform (DCT) and Histogram of
Oriented Gradients (HOG) are computed to represent the handwritten words. The
dimensionality of the HOG features is reduced by applying Principal Component
Analysis (PCA). Each set of features is separately fed to two different
classifiers, support vector machine (SVM) and fuzzy k-nearest neighbor (FKNN)
giving a total of four independent classifiers. A set of different fusion rules
is applied to combine the output of the classifiers. The proposed scheme
evaluated on the IFN/ENIT database of Arabic handwritten words reveal that
combining the classifiers results in improved recognition rates which, in some
cases, outperform the state-of-the-art recognition systems.
Keywords: Handwritten Arabic word recognition; Classifier combination; Support vector machine; Fuzzy K-nearest neighbor; Discrete cosine transform; Histogram of oriented gradients.
Received September 22, 2014; accepted August 31, 2015