Many of today’s recognition approaches for hand-drawn sketches are feature-based, which is conceptually similar to the recognition of hand-written text. While very suitable for the latter (and more tasks, e.g., for entering gestures as commands), such approaches do not easily allow for clustering and segmentation of strokes, which is crucial to their recognition. This results in applications which do not feel natural but impose artificial restrictions on the user regarding how sketches and single components (shapes) are to be drawn. This paper proposes a concept and architecture for a generic geometry-based recognizer. It is designed for the mentioned issue of clustering and segmentation. All strokes are fed into independent preprocessors called transformers that process and abstract the strokes. The result of the transformers is stored in models. Each model is responsible for a certain type of primitive, e.g., a line or an arc. The advantage of models is that different interpreta...