Intelligent Reading of English Text Based on the Generative Model Constraint Label Fusion.

The intelligent reading of English text is affected by complex environmental factors, which will result in low reading accuracy and poor reader experience. Based on the artificial intelligence model, this study constructs the artificial intelligence English text reading model by using the generative model constraint label, which helps to improve the intelligence of the English text reading effect. This study also designs a multigraph label fusion algorithm based on generative model constraints. By making full use of the prior knowledge of multiple graphs, the result of fusion graph segmentation is achieved. Moreover, this study also uses the combination of two algorithms, namely, the combination of GMM and MRF, to express the spatial correlation of local statistical features and image pixels in a comprehensive and all-round way. Another model design also includes a series of joint distributions of the learning data for the construction of the image energy function, and the conditional probability distribution is used as the model for prediction. At the end of the study, another variable control experiment is carried out to analyze the performance of the model and the accuracy of the model in English text recognition and classification is studied and counted. The research results show that the intelligent reading model constructed based on this study can meet the needs of the actual situation.