Scientific challenges underlying production document processing

The field of Document Recognition is bipolar. On one end lies the excellent work of academic institutions engaging in original research on scientifically interesting topics. On the other end lies the document recognition industry which services needs for high-volume data capture for transaction and back-office applications. These realms seldom meet, yet the need is great to address technical hurdles for practical problems using modern approaches from the Document Recognition, Computer Vision, and Machine Learning disciplines. We reflect on three categories of problems we have encountered which are both scientifically challenging and of high practical value. These are Doctype Classification, Functional Role Labeling, and Document Sets. Doctype Classification asks, "What is the page I am looking at?" Functional Role Labeling asks, "What is the status of text and graphical elements in a model of document structure?" Document Sets asks, "How are pages and their contents related to one another?" Each of these has ad hoc engineering approaches that provide 40-80% solutions, and each of them begs for a deeply grounded formulation both to provide understanding and to attain the remaining 20-60% of practical value. The practical need is not purely technical but also depends on user experience and, therefore, the art of Design.