Graphical Input Through Machine Recognition of Sketches

Summary:

Herot's short paper gave a brief, but comprehensive, look at sketch interaction systems in the mid 70s.

The paper first looks at a general recognizer, HUNCH, that tries to see if accurate knowledge can be obtained without using a specific domain. The system takes data drawn on a large tablet with a special pencil, and the raw input data is recorded by the computer. The HUNCH system used another application, called STRAIT, that found corners in data by examining the user's pen speed. The system also used a process called latching to snap endpoints of close lines together. Unfortunately, the HUNCH system had problem with consistency between different users. Users drawing at different pen speeds produced different corners, and the latching technique sometime distorted an intended image, such as oversnapping lines in a cube. The system also handles overtraced lines by merging lines together, provides some 3D image inference through unexplained techniques, and can create floor maps by looking at boxed rooms and doorways.

Context is an important part of a sketch, and Herot recognizes this fact by mentioning how data interpretations should have context information. The context should be specified to the computer as to avoid issues of recognizing the domain. Herot briefly mentions a top-down processing for recognizing sketches with a context architecture.

Lastly, Herot mentions that user input is a key component of a sketch recognition system that should not be ignored. More complex interfaces need to be developed so that a user can interact with a program and correct mistakes, and corner finding algorithms need to be tuned for an individual user.

Discussion:

Although none of the topics mentioned in Herot are new to me, the fact that all of these issues were mentioned in a paper written in 1976 is surprising. For instance, I had been under the assumption that using pen speed to detect corners was a relatively new fad.

I also am very surprised that the system tried (and from the one example, succeeded) at incorporating 3D image analysis. I remember reading a paper about using edges and vertices to detect whether an image is 3D, but I cannot seem to recall the author involved, so it's hard for me to construct a timeline for that research.