Intelligent document language classification and web based translation system

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Recent developments on information and communications technologies help globally and important to access, share, translate and the documents use easily and effectively via internet media. Language identification is an important task for web information retrieval services. Automatic language identification and translation have become increasingly important, as more and more documents are being served on internet within many languages. This study presents new methods to identify web contents, containing MS Word, PDF and HTML documents in different languages and to translate them into specified languages. The identification problem can be seen as a specific instance of the more general problem of an item classification through its attributes in a limited workspace. This novel approach is based on artificial neural network model to recognize the languages. Documents content belonging to 15 languages were used in test with a new testing methodology and translating them into 64 languages automatically for language processing. The results have shown that the approaches presented in this work are very successful to meet the expectations in real-time language identification and translation accuracy and reduce the number of letters in solution space in comparison with the available two methods.

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Language identification , language translation , web based application , artificial neural network

Erciyes University Journal Of The Institute Of Science an‎d Technology

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Erciyes University Journal Of The Institute Of Science an‎d Technology