Translingual Image-to-Text Conversion: Bridging Visual and Multilingual Semantic Representations

This paper presents a novel approach for translingual image-to-text conversion to establish a connection between visual content and multilingual semantic representations. The suggested strategy makes use of deep learning methods to extract valuabledata from images and subsequently generates linguistically diverse textual descriptions in multiple languages. By bridging the gap between visual and linguistic domains, our approach offers a comprehensive solution to the challenge of conveying image content across different languages. Experimental results illustrate the suggested method's efficiency in accurately converting images to multilingual text, thus contributing to enhanced cross-lingual information retrieval and content accessibility.