Over the last few decades, significant strides have been made in handwriting recognition (HR), which is the automatic transcription of handwritten documents. HR often focuses on modern handwritten material, but in the electronic age, the volume of handwritten material is rapidly declining. However, we believe HR is on the verge of having major application to historical record collections. In recent years, archives and genealogical organizations have conducted huge campaigns to transcribe valuable historical record content with such transcription being largely done through human-intensive labor. HR has the potential of revolutionizing these transcription endeavors. To test the hypothesis that this technology is close to applicability, and to provide a testbed for reducing any accuracy gaps, we have developed an evaluation paradigm for historical record handwriting recognition. We created a huge test corpus consisting of four historical data collections of four differing genres and three languages. In this paper, we provide the details of these extensive resources which we intend to release to the research community for further study. Since several research organizations have already participated in this evaluation, we also show initial results and comparisons to human levels of performance.

Previous content extraction evaluations have neglected to address problems which complicate the incorporation of extracted information into an existing knowledge base. Previous question answering evaluations have likewise avoided tasks such as explicit disambiguation of target entities and handling a fixed set of questions about entities without previous determination of possible answers. In 2009 NIST conducted a Knowledge Base Population track at its Text Analysis Conference to unite the content extraction and question answering communities and jointly explore some of these issues. This exciting new evaluation attracted 13 teams from 6 countries that submitted results in two tasks, Entity Linking and Slot Filling. This paper explains the motivation and design of the tasks, describes the language resources that were developed for this evaluation, offers comparisons to previous community evaluations, and briefly summarizes the performance obtained by systems. We also identify relevant issues pertaining to target selection, challenging queries, and performance measures.