Authors:
Rihab Dhief
1
;
Rabaa Youssef
2
;
1
and
Amel Benazza
1
Affiliations:
1
University of Carthage SUP’COM, LR11TIC01, COSIM Lab., 2083, El Ghazala, Tunisia
;
2
INSAT, University of Carthage, Tunisia
Keyword(s):
Handwritten Arabic Character Recognition, Skeletonization, Freeman Chain Code, Heutte Descriptors, Feature Extraction, Supervised Machine Learning Algorithms, Deep Learning.
Abstract:
The Arabic handwritten character recognition is a research challenge due to the complexity and variability of forms and writing styles of the Arabic alphabet. The current work focuses not only on reducing the complexity of the feature extraction step but also on improving the Arabic characters’ classification rate. First, we lighten the preprocessing step by using a grayscale skeletonization technique easily adjustable to image noise and contrast. It is then used to extract structural features such as Freeman chain code and Heutte descriptors. Second, a new model using the fusion of results from machine learning algorithms is built and tested on two grayscale images’ datasets: IFHCDB and AIA9K. The proposed approach is compared to state-of-the-art methods based on deep learning architecture and highlights a promising performance by achieving an accuracy of 97.97% and 92.91% respectively on IFHCDB and AIA9K datasets, which outperforms the classic machine learning algorithms and the de
ep neural network chosen architectures.
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