sketch

This paper offers a solution to the mode problem in computer sketch/notetaking programs. Conventionally, the user must specify the intended "draw" or "command" mode prior to performing a stroke. This necessity has proven to be a barrier to the usability of pen/stylus systems. We offer a novel Inferred-Mode interaction protocol that avoids the mode hassles of conventional sketch systems. The system infers the user's intent, if possible, from the properties of the pen trajectory and the context of the trajectory. If the intent is ambiguous, the user is offered a choice mediator in the form of a pop-up button. To maximize the fluidity of drawing, the user is entitled to ignore the mediator and continue drawing. We present decision logic for the inferred mode protocol, and discuss subtleties learned in the course of its development. We also present results of initial user trials validating the usability of this interaction design.

We present SketchREAD, a multi-domain sketch recognition engine capable of recognizing freely hand-drawn diagrammatic sketches. Current computer sketch recognition systems are difficult to construct, and either are fragile or accomplish robustness by severely limiting the designer's drawing freedom. Our system can be applied to a variety of domains by providing structural descriptions of the shapes in that domain; no training data or programming is necessary. Robustness to the ambiguity and uncertainty inherent in complex, freely-drawn sketches is achieved through the use of context. The system uses context to guide the search for possible interpretations and uses a novel form of dynamically constructed Bayesian networks to evaluate these interpretations. This process allows the system to recover from low-level recognition errors (e.g., a line misclassified as an arc) that would otherwise result in domain level recognition errors. We evaluated Sketch-READ on real sketches in two domains--family trees and circuit diagrams--and found that in both domains the use of context to reclassify low-level shapes significantly reduced recognition error over a baseline system that did not reinterpret low-level classifications. We also discuss the system's potential role in sketch based user interfaces.

The human visual system makes a great deal more of images than the elemental marks on a surface. In the course of viewing, creating, or editing a picture, we actively construct a host of visual structures and relationships as components of sensible interpretations. This paper shows how some of these computational processes can be incorporated into perceptually-supported image editing tools, enabling machines to better engage users at the level of their own percepts. We focus on the domain of freehand sketch editors, such as an electronic whiteboard application for a pen-based computer. By using computer vision techniques to perform covert recognition of visual structure as it emerges during the course of a drawing/editing session, a perceptually supported image editor gives users access to visual objects as they are perceived by the human visual system. We present a flexible image interpretation architecture based on token grouping in a multiscale blackboard data structure. This organization supports multiple perceptual interpretations of line drawing data, domain-specific knowledge bases for interpretable visual structures, and gesture-based selection of visual objects. A system implementing these ideas, called PerSketch, begins to explore a new space of WYPIWYG (What You Perceive Is What You Get) image editing tools.

A long standing challenge in pen-based computer interaction is the ability to make sense of informal sketches. A main difficulty lies in reliably extracting and recognizing the intended set of visual objects from a continuous stream of pen strokes. Existing pen-based systems either avoid these issues altogether, thus resulting in the equivalent of a drawing program, or rely on algorithms that place unnatural constraints on the way the user draws. As one step toward alleviating these difficulties, we present an integrated sketch parsing and recognition approach designed to enable natural, fluid, sketch-based computer interaction. The techniques presented in this paper are oriented toward the domain of network diagrams. In the first step of our approach, the stream of pen strokes is examined to identify the arrows in the sketch. The identified arrows then anchor a spatial analysis which groups the uninterpreted strokes into distinct clusters, each representing a single object. Finally, a trainable shape recognizer, which is informed by the spatial analysis, is used to find the best interpretations of the clusters. Based on these concepts, we have built SimuSketch, a sketch-based interface for Matlab's Simulink software package. An evaluation of SimuSketch has indicated that even novice users can effectively utilize our system to solve real engineering problems without having to know much about the underlying recognition techniques.