Movies offer users a huge range of visual information like attractive stories. The traditional approaches have demonstrated that knowing about movie stories via only visual information is complicated. The natural data of graphical texts play a significant task in portraying information in various domains like entertainment, education, communication, etc. Similarly, script generation by considering the earlier dialogue is more complex owing to the inherent nature of the text. Text identification in natural data considers script identification that needs localization of text. Especially for natural scene data, script generation is a complicated task due to different background/foreground components. The texts comprise varied textures, variations in size, orientation, colors, and fonts. It is essential to have a system that produces scripts or stories from a storyline automatically. However, this research is not emerging nowadays. While considering the dialogue systems would also help drive dialogues through a dialogue plan. Hence, a new movie script generation model is suggested through processing the movie text data. Initially, the text data is collected related to a different movie that consists of characters, scenes, genre, location, etc. Secondly, the data pre-processing is carried out to enhance data quality. Further, the significant features are extracted from the pre-processed data through Term Frequency-Inverse Document Frequency (TFIDF) and word2vector. The deep features are extracted to get the noteworthy features using a Deep Belief Network (DBN) from RBN layers. Finally, the deep features are given to the Ensemble-based Movie Scrip Generation (EMCG), where the Optimized hybrid script generation process using ensemble learning is performed by Bidirectional Long Short-Term Memory (Bi-LSTM), Generative Pre-Trained Transformer version 3 (GPT3), and GPT Neo X models, where the parameters of deep learning algorithms are optimized using the Adaptively Improved Cat and Mouse-based Optimizer (AI-CMO) algorithm. Here, the outcomes are attained through taking averaging among the classified outcomes. The standard performance measures are used for evaluating the effectiveness of the proposed method.
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