A Novel Framework for Multilingual Script Detection and Pattern Analysis in Mixed Script Queries

A script detection system that is capable of handling several languages is becoming more necessary in today's world. The task of identifying scripts written in various languages has been substantially facilitated by the use of machine learning and deep learning, respectively. Machine learning techniques have used the Naive Bayes and Support Vector Machines (SVM) mechanism for the purpose of language detection. On the other hand, this paper reviews several unique deep-learning processes that have considered a range of methodologies, including LSTM and Bert. On the other hand, it has been shown that there is a need to improve the accuracy and the scalability often incorporated in multilingual systems. As a consequence of this, the primary focus of the present investigation is on the development of an innovative framework that is capable of recognizing scripts in a variety of languages. In addition, this technique considers pattern analysis while considering mixed script queries. A scalable, efficient, and adaptive approach has been established via study to increase the accuracy of the identification of a large number of languages. Accuracy, recall, and F1-score are some of the performance metrics that have been calculated in order to evaluate the efficacy of the multilingual script identification that has been presented. In conclusion, it has been found that the approach that was provided has supplied a solution that is both efficient and scalable for the detection of multilingual scripts.