Bringing Semantics in Word Image Retrieval

Performance of the recognition free approaches for document retrieval, heavily depends on the exact or approximate matching of images (in some feature space) to retrieve documents containing the same word. However, the harder problem in information retrieval is to effectively bring semantics into the retrieval pipeline. This is further challenging when the matching is based on visual features. In this work, we investigate this problem, and suggest a solution by directly transferring the semantics from the textual domain. Our retrieval framework uses (i) the language resources like Word Net and (ii) an annotated corpus of document images, to retrieve semantically relevant words from a large word image database. We demonstrate the method on two languages - English and Hindi, and quantitatively evaluate the performance on annotated word image databases of more than a Million images.