Çizim vuruşlarının bölütlenmesi ve çizim tanıma için bütünleşik bir yaklaşım

Hardware supporting pen-based interaction have been around for a long time, however progress in efficient and intelligent processing of input has been lagging far behind. This is partly due to the complicated nature of the sketch recognition problem. Optimal sketch recognition is intractable even for moderate-sized sketches. Recent methods deal with the problem either by making simplifying assumptions or by adopting sub-optimal methods. In this thesis, as an alternative to the sub-optimal methods, we describe an optimal and polynomial-time trainable framework for multi-domain sketch recognition. Our solution handles offline and interspersed sketches as well as online sketches, and it does not make assumptions about user input. Our unified framework is based on supervised machine learning techniques, graph theory, and dynamic programming. We apply the framework to two fundamental problems of bottom-up sketch recognition: stroke fragmentation and sketch segmentation. Dynamic programming approach is directly applicable to the fragmentation of strokes and segmentation of ordered primitives. For other cases, such as offline and interspersed sketches, we introduce the \textit{spatial serialization} concept to impose an order on the primitives. We propose different graph theoretic methods and coherence models to convert 2D points into an ordered set of primitives. We evaluate the accuracy and runtime of different serialization schemes on multiple datasets. For both fragmentation and segmentation problems, experiments show that the accuracy of the unified framework either matches with the state-of-the-art, or it surpasses them by a large margin.