Complementary Approaches Built as Web Service for Arabic Handwriting OCR Systems via Amazon Elastic MapReduce (EMR) Model
Hassen Hamdi1, Maher Khemakhem2, and Aisha Zaidan1
1Department
of Computer Science, Taibah University, Kingdom of Saudi Arabia
2Department of Computer
Science, University of King Abdul-Aziz, Kingdom of Saudi Arabia
Abstract: Arabic Optical Character Recognition (OCR) as Web
Services represents a major challenge for handwritten document recognition. A
variety of approaches, methods, algorithms and techniques have been proposed in
order to build powerful Arabic OCR web services. Unfortunately, these methods could
not succeed in achieving this mission in case of large quantity Arabic handwritten
documents. Intensive experiments and observations revealed that some of the
existing approaches and techniques are complementary and can be combined to
improve the recognition rate. Designing and implementing these recent sophisticated
complementary approaches and techniques as web services are commonly complex; they
require strong computing power to reach an acceptable recognition speed
especially in case of large quantity documents. One of the possible solutions
to overcome this problem is to benefit from distributed computing architectures
such as cloud computing. This paper describes the design and implementation of
Arabic Handwriting Recognition as a web service (AHRweb service) based on the
complementary approach K-Nearest Neighbor (KNN) /Support Vector Machine (SVM)
(K-NN/SVM) via Amazon Elastic Map Reduce (EMR) model. The experiments were
conducted on a cloud computing environment with a real large scale handwriting
dataset from the Institut Für Nachrichtentechnik (IFN)/ Ecole Nationale
d’Ingénieur de Tunis (ENIT) IFN/ENIT database. The J-Sim (Java Simulator) was
used as a tool to generate and analyze statistical results. Experimental
results show that Amazon Elastic Map Reduce (EMR) model constitutes a very
promising framework for enhancing large Arabic Handwriting Recognition (AHR) web
service performances.
Keywords: Arabic
handwriting, complementary approaches and techniques, K-NN/SVM, web service, amazon
elastic mapreduce.
Received April 25, 2015; accepted January 3, 2016