Applications in Explainable Text Identification and Classification in Multilingual Document Processing

Multilingual document classification has grown in importance as information systems have become more global. Despite their effectiveness, classic AI models frequently operate as opaque black boxes; efforts are being made to increase the models' explainability. Fuzzy logic and membership functions are used to model linguistic ambiguity and uncertainty, enabling interpretable feature representation and reasoning. This paper presents several applications of multilingual text extraction and classification using XAI and machine learning. It provides a contextual foundation for XAI-based text identification in multilingual document classification in diverse application areas. Furthermore, it addresses various applications, highlighting the languages used and the document type.