Algorithms for clustering data
Algorithms for clustering data
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Density-Based Multiscale Data Condensation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handling Spatial Information in On-Line Handwriting Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Contextual Recognition of Hand-Drawn Diagrams with Conditional Random Fields
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
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In this paper, we present a new approach that explicitly exploits the spatial context of strokes to drive the shape recognition. We call this recognition method "context driven recognition" (CDR). The underlying idea is that only a sub-set of all possible symbols can be recognized in a specific spatial context. The main challenge is to detect and model automatically the context areas of interest so that the recognition method can be independent of any specific information on the targeted pen-based application. The paper details the learning scheme of the CDR method and how the obtained model is used during the recognition process. The results on a real-world pen-based recognition problem show that the method can reach better performances than a classical approach by decreasing the shape recognition complexity.