An Online Hand-Drawn Electric Circuit Diagram Recognition System Using Hidden Markov Models

In this paper we experiment the capabilities of Hidden Markov Models (HMM) to model the time-variant signal produced by the movement of a pen when drawing a sketch such as an electrical circuit diagram. We consider that the sketches have been generated by a two-level stochastic process. The underlying process governs the stroke production from a neuro-motor control point of view: go straight, change direction, produce a curve. A second stochastic process delivers the observed signal, which is a sequence of sampled points. Three different architectures of HMM are proposed and compared. On a dataset of 100 hand-drawn sketches, the proposed method allows to classify correctly more than 83% of the points with respect to the connector and symbol classes.