Many companies still operate critical business processes using paper-based forms, including customer surveys, inspections, contracts and invoices. Converting those handwritten forms to symbolic data is expensive and complicated. This paper presents an overview of the Image-Based Document Management (IBDM) system for analyzing handwritten forms without requiring conversion to symbolic data. Strokes captured in a questionnaire on a tablet are separated into fields that are then displayed in a spreadsheet. Rows represent documents while columns represent corresponding fields across all documents. IBDM allows a process owner to capture and analyze large collections of documents with minimal IT support. IBDM supports the creation of filters and queries on the data. IBDM also allows the user to request symbolic conversion of individual columns of data and permits the user to create custom views by reordering and sorting the columns. In other words, IBDM provides a "writing on paper" experience for the data collector and a web-based database experience for the analyst.

One common use for interactive whiteboards (IWBs) is to mark up content provided from a connected laptop. Typically a marking layer is provided which is independent of the laptop content. This leads to problems when the laptop content changes while the strokes in the mark up layer do not. The LiveStroke prototype described in this document uses computer vision techniques to associate the marks with the image of the underlying content from the laptop. For instance, if marks are made on the first page of a document, those marks disappear when the laptop user scrolls to a different page. The marks reappear in the right location on the page when the user returns to the first page. While we have integrated these techniques with interactive whiteboards the techniques are also applicable to screen sharing with mobile touch devices and projectors.

A smart display detection system is proposed that allows users to connect with displays using mobile cameras. The smart displays dynamically update a server with information about the current screen content and the system matches captured images from mobile devices with the screen information. A synchronized timestamped matching strategy is employed to achieve high performance in detecting screens playing motion intensive video and an aggressive feature selection method is used to minimize bandwidth requirements.

This paper describes the hardware, software, and a document model for a prototype E-Writer. Paper like displays have proved useful in E-Readers like the Kindle in part because of low power usage and the ability to read indoors and out. We focus on emulating other properties of paper in the E-Writer: everyone knows how to use it, and users can write anywhere on the page. By focusing on a simple document model consisting primarily of images and strokes we enabled rapid application development that integrates easily with current paper-based document workflows. This paper includes preliminary reports on usage of the E-Writer and its software by a small test group.