  Recognition and retrieval of textual content from the large document
collections have been a powerful use case for the document image analysis
community. Often the word is the basic unit for recognition as well as
retrieval. Systems that rely only on the text recogniser (OCR) output are not
robust enough in many situations, especially when the word recognition rates
are poor, as in the case of historic documents or digital libraries. An
alternative has been word spotting based methods that retrieve/match words
based on a holistic representation of the word. In this paper, we fuse the
noisy output of text recogniser with a deep embeddings representation derived
out of the entire word. We use average and max fusion for improving the ranked
results in the case of retrieval. We validate our methods on a collection of
Hindi documents. We improve word recognition rate by 1.4 and retrieval by 11.13
in the mAP.
