A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Language identification in web pages
Proceedings of the 2005 ACM symposium on Applied computing
A Vector Space Modeling Approach to Spoken Language Identification
IEEE Transactions on Audio, Speech, and Language Processing
ART-EMAP: A neural network architecture for object recognition by evidence accumulation
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
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.