Visual Word Embedding for Text Classification

The question we answer with this paper is: ‘can we convert a text document into an image to take advantage of image neural models to classify text documents?’ To answer this question we present a novel text classification method that converts a document into an encoded image, using word embedding. The proposed approach computes the Word2Vec word embedding of a text document, quantizes the embedding, and arranges it into a 2D visual representation, as an RGB image. Finally, visual embedding is categorized with state-of-the-art image classification models. We achieved competitive performance on well-known benchmark text classification datasets. In addition, we evaluated our proposed approach in a multimodal setting that allows text and image information in the same feature space.