The article discusses approaches to text recognition based on artificial intelligence algorithms. Since text language identification is a classification problem in artificial intelligence, the article examines the capabilities of several classification algorithms using machine learning and neural network models. The study addresses the issue of forming a training dataset for a model that identifies Uzbek, English, Russian, and Karakalpak languages. Additionally, a comparative analysis of the accuracy indicators of the models used for text language identification is conducted.
| Mualliflar | Xujayarov, Ilyos, Ochilov, Mannon, Xolmatov, Orzimurod, Jurayev, Dilshod |
|---|---|
| Jurnal | Рақамли технологияларнинг назарий ва амалий масалалари |
| Nashr sanasi | 2024-06-10 |
| Jild | 7 |
| Son | 2 |
| Betlar | 59-67 |
| Til | Rus |
| DOI | 10.62132/ijdt.v7i2.182 |
DOI: 10.62132/ijdt.v7i2.182 · Maqolaning asl sahifasi
NLP, crowling, matn tili, o‘quv ma’lumotlar to‘plami, mashinali o‘qitish, sinflashtirish, chuqur o‘qitish, rekurrent neyron tarmoqlar, model aniqligini baholash, NLP, crowling, text language, training dataset, machine learning, classification, deep learning, recurrent neural networks, model accuracy evaluation
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