Authors:
Taraggy Ghanim
1
;
Mahmoud I. Khalil
2
and
Hazem M. Abbas
2
Affiliations:
1
Faculty of Computer Science, Misr International University, Egypt, Faculty of Engineering, Ain Shams University and Egypt
;
2
Faculty of Engineering, Ain Shams University and Egypt
Keyword(s):
Arabic Handwriting Recognition, Random Forest, Kullback-Leibler Divergence, Pyramid Histogram of Gradient, Support Vector Machine.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Feature Selection and Extraction
;
Pattern Recognition
;
Shape Representation
;
Software Engineering
;
Theory and Methods
Abstract:
Automatic Recognition of Arabic Handwriting is a pervasive field that has many challenging complications to solve. Such complications include big databases and complex computing activities. The proposed approach is a multi-stage cascading recognition system bases on applying Random Forest classifier (RF) to construct a forest of decision trees. The constructed decision trees split big databases to multiple smaller data-mined sets based on the most discriminating computed geometric and regional features. Each data-mined set include similar database classes. RF match each test image with one of the data-mined sets. Afterwards, the matching classes are sorted relative to test image using Pyramid Histogram of Gradients and Kullback-Leibler based ranking algorithm. Finally, the classification process is applied on the highly ranked matching classes to assign a class membership to test image. Adjusting the classification process to only consider the highly ranked database classes reduced t
he computing classification and enhanced the overall performance. The proposed approach was tested on IFN-ENIT Arabic database and achieved satisfactory results and enhanced sensitivity of decision trees to reach 93.5% instead of 86.5% (Ghanim et al., 2018).
(More)