@article {10.3844/jcssp.2006.879.884,
article_type = {journal},
title = {Probabilistic Artificial Neural Network For Recognizing the Arabic Hand Written Characters },
author = {khatatneh, Khalaf and El Emary, Ibrahiem M.M. and Al-Rifai, Basem},
volume = {2},
number = {12},
year = {2006},
month = {Dec},
pages = {879-884},
doi = {10.3844/jcssp.2006.879.884},
url = {https://thescipub.com/abstract/jcssp.2006.879.884},
abstract = {The objective of this study was to present a new technique assists in developing a recognition system for handling the Arabic Hand Written text. The proposed system is called Arabic Hand Written Optical Character Recognition (AHOCR). AHOCR was concerned with recognition of hand written Alphanumeric Arabic characters. In the present work, extracted characters are represented by using geometric moment invariant of order three. The advantage of using moment invariant for pattern classification as compared to the other methods was its invariant with respect to its: position , size and rotation .The proposed technique was divided into three major steps : the first step was concerned with digitization and preprocessing documents to create connect components, detect the skew of characters and correct it .The second step deals with how to use geometric moment invariant features of the input Arabic characters to extract features . The third step focused on description of an advanced system of classification using Probabilistic Neural networks structure which yields significant speed improvement. Our final results indicate and clarify that the proposed AHOCR technique achieves an excellent test accuracy of recognition rated up to 97% for isolated Arabic characters and 96% for Arabic text.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}