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The goal of this work is to bring semantics into the tasks of textrecognition and retrieval in natural images. Although text recognition andretrieval have received a lot of attention in recent years, previous works havefocused on recognizing or retrieving exactly the same word used as a query,without taking the semantics into consideration.  In this paper, we ask the following question: \emph{can we predict semanticconcepts directly from a word image, without explicitly trying to transcribethe word image or its characters at any point?} For this goal we propose aconvolutional neural network (CNN) with a weighted ranking loss objective thatensures that the concepts relevant to the query image are ranked ahead of thosethat are not relevant. This can also be interpreted as learning a Euclideanspace where word images and concepts are jointly embedded. This model islearned in an end-to-end manner, from image pixels to semantic concepts, usinga dataset of synthetically generated word images and concepts mined from alexical database (WordNet). Our results show that, despite the complexity ofthe task, word images and concepts can indeed be associated with a high degreeof accuracy

LEWIS: Latent Embeddings for Word Images and their Semantics