A framework to access handwritten information within large digitized paper collections

Abstract

We describe our efforts with the National Archives and Records Administration (NARA) to provide a form of automated search of handwritten content within large digitized document archives. With a growing push towards the digitization of paper archives there is an imminent need to develop tools capable of searching the resulting unstructured image data as data from such collections offer valuable historical records that can be mined for information pertinent to a number of fields from the geosciences to the humanities. 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 the 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 describe these steps, the open source framework we have developed, and how it can be used not only on the recently released 1940 Census data containing nearly 4 million high resolution scanned forms, but also on other collections of forms. With a growing demand to digitize our wealth of paper archives we see this type of automated search as a low cost scalable alternative to the costly manual transcription that would otherwise be required.