Arabic Script Language Identifications using Adaptive Neural Network

A. Selamat and C.-C. Ng (Malaysia)


Adaptive neural networks, Fuzzy ARTMAP, Default ARTMAP, letter frequency, language identification, and Arabic script


Globalization has led to unlimited information between geographically remote locations and insight of a global common market. When constructing website applications for use on various industries, developers need to deal with a wide range of users from different countries. Thus, multilingual system is implemented in order to make available multilingual environment in those applications. However, it is time-consuming to define all the possible languages for multilingual system manually, it would be desirable to automate the adoption of language identification for text-based documents. To address this need, we introduce language identification of Arabic script documents with letter frequency based. Techniques used for identification are fuzzy ARTMAP and default ARTMAP, which are belong to neural network architectures that perform incremental supervised learning. Arabic script documents such as Arabic, Persian and Urdu were used for performing language identification. From the experiments, we have found that fuzzy ARTMAP has performed better than the default ARTMAP in Arabic script language identification.

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