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Reseach Article

Dynamic with Dictionary Technique for Arabic Text Compression

by Fatima Thaher Ahmad Aburomman
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 135 - Number 9
Year of Publication: 2016
Authors: Fatima Thaher Ahmad Aburomman
10.5120/ijca2016908299

Fatima Thaher Ahmad Aburomman . Dynamic with Dictionary Technique for Arabic Text Compression. International Journal of Computer Applications. 135, 9 ( February 2016), 4-9. DOI=10.5120/ijca2016908299

@article{ 10.5120/ijca2016908299,
author = { Fatima Thaher Ahmad Aburomman },
title = { Dynamic with Dictionary Technique for Arabic Text Compression },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 135 },
number = { 9 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 4-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume135/number9/24075-2016908299/ },
doi = { 10.5120/ijca2016908299 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:35:17.943758+05:30
%A Fatima Thaher Ahmad Aburomman
%T Dynamic with Dictionary Technique for Arabic Text Compression
%J International Journal of Computer Applications
%@ 0975-8887
%V 135
%N 9
%P 4-9
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this research paper we build a new, reliable, and sufficient algorithm for Arabic text language. The proposed algorithm should combine the features of the Huffman and Lempel Ziv algorithms, and is expected be able to reduce the general compression ratio. Our approach is different from Huffman algorithm in the sense that it assigns codes to n-gram symbols where n is a positive integer that is greater than or equal to one. Compared to Huffman algorithm, which assigns a code to each symbol individually, our approach is expected to assign codes to symbols in average. Our approach is different from Lempel Ziv algorithm in the sense that the size of dictionary that we build does not grow in an uncontrolled manner. The size of the dictionary is fixed and its size can be expected prior to process the text files that are to be compressed. This is because the size of each word in the dictionary we build is fixed and is equal to n. So for example, given that the number of different symbols in the text file at hand is m and that n is 2, the total number of entries in the dictionary that we propose to build will be m*m in the worst case.

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Index Terms

Computer Science
Information Sciences

Keywords

Data compression Text compression Arabic Text File data compression.