What is GestureLab?

GestureLab is a software tool designed to facilitate rapid development and testing of domain specific gesture recognizers. Recognizers can be developed entirely
within GestureLab without any need for a testbed application and can be coupled with grammar engines such as Cider without modification.

GestureLab recognizers follow the standard approach to statistical gesture recognition: recognition is performed on digital ink which includes position, timing,
pressure, and angle data. Statistical summary features such as the total length of the gesture, initial stroke angle, and maximum curvature are extracted from this
data and used by a classifier algorithm to predict class labels (gesture types). A recognizer thus consists of a bundle of feature extractors and a classifier
algorithm trained on a particular gesture corpus.

GestureLab supports all phases of the recognizer development process: (a) collecting, manipulating and sharing gesture corpora, and (b) automatic training and
cross-validation of feature extraction and recognizer mechanisms. In the event that the built-in feature extraction and recognizer mechanisms are insufficient,
GestureLab also allows the developer to readily define (c) new feature extraction mechanisms and (d) new recognizer algorithms. GestureLab generates probabilistic
recognizers that return membership probabilities for all possible token classes instead of a single most likely class. Probabilistic classificators can be useful
during disambiguation at a syntactic level.

Version 1.0-alpha4

GestureLab-1.0a4.zip (18.8Mb) contains the GestureLab application for Mac OS X (10.4 onwards) and a default database of gestures.
digest.zip (15.8Mb) contains all GestureLab-1.0a4 source code.
librecognizer.zip (600kb) contains the source code for librecognizer, a library to develop and integrate the default GestureLab plugins.
Documentation (627Kb) for this version.