Actuation in perception: Character classification in engineering drawings

We propose a novel approach to 2D character recognition by incorporating actuation data into the shape representation. Sensorimotor data is analyzed in terms of actuation sequences which generate the data. We illustrate the use of Wreath Products (WPs) to represent robot sensorimotor experience in a way that ties together perception and actuation. WPs naturally represent not only the Euclidean symmetries possessed by an object, but also the sequence of actuations used to generate those. Two distinct approaches using actuation signals to represent shape are compared: (1) the Kullback-Leibler measure is applied to histograms of translation symmetries in the shape, and (2) a distance metric is defined on pure actuation signals. Experimental results show that these methods achieve excellent classification rates (99 %) on text extracted from scanned images of engineering drawings for the top five hypotheses.