Open AccessA Design of a Hybrid Algorithm for Optical Character Recognition of Online Hand-Written Arabic Alphabets
Author(s) -
Waleed Noori Hussein,
Haider N. Hussain
Publication year - 2019
Publication title -
iraqi journal of science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.152
H-Index - 4
eISSN - 2312-1637
pISSN - 0067-2904
DOI - 10.24996/ijs.2019.60.9.22
Subject(s) - computer science , optimal distinctiveness theory , character (mathematics) , intelligent character recognition , arabic , optical character recognition , artificial intelligence , speech recognition , artificial neural network , decision tree , pattern recognition (psychology) , identification (biology) , natural language processing , relevance (law) , arabic numerals , character recognition , linguistics , mathematics , image (mathematics) , psychology , philosophy , botany , geometry , political science , law , psychotherapist , biology
The growing relevance of printed and digitalized hand-written characters has necessitated the need for convalescent automatic recognition of characters in Optical Character Recognition (OCR). Among the handwritten characters, Arabic is one of those with special attention due to its distinctive nature, and the inherent challenges in its recognition systems. This distinctiveness of Arabic characters, with the difference in personal writing styles and proficiency, are complicating the effectiveness of its online handwritten recognition systems. This research, based on limitations and scope of previous related studies, studied the recognition of Arabic isolated characters through the identification of its features and dots in view of producing an efficient online Arabic handwriting isolated character recognition system. It proposes a hybrid of decision tree and Artificial Neural Network (ANN), as against being combined with other algorithms as found in previous studies. The proposed recognition process has four main steps with associated sub-steps. The results showed that the proposed method achieved the highest performance at 96.7%, whereas the benchmark methods which are EDMS and Naeimizaghiani had 68.88% and 78.5 % respectively. Based on this, ANN has the best performance recognition rate at 98.8%, while the best rate for decision tree was obtained at 97.2%.