We describe our efforts to provide a form of automated search of handwritten content for digitized document archives. To carry out the search we use a computer vision technique called word spotting. A form of content based image retrieval, it avoids the still difficult task of directly recognizing text by allowing a user to search using a query image containing handwritten text and ranking a database of images in terms of those that contain more similar looking content. In order to make this search capability available on an archive three computationally expensive pre-processing steps are required. We augment this automated portion of the process with a passive crowd sourcing element that mines queries from the systems users in order to then improve the results of future queries. We benchmark the proposed framework on 1930s Census data, a collection of roughly 3.6 million forms and 7 billion individual units of information.