Segmentation Based Online Word Recognition: A Conditional Random Field Driven Beam Search Strategy

We propose a segmentation based online word recognition approach which uses a Conditional Random Field (CRF) driven beam search strategy. An efficient trie-lexicon directed, breadth-first beam search algorithm is employed in a combined segmentation-and-recognition framework to accomplish real-time recognition of online handwritten cursive English words. This framework is developed by building a candidate lattice of primitive segments obtained through over segmentation of the word pattern. The search space for the lattice is expanded by synchronously matching the lattice nodes to likely character patterns from a trie-dictionary constructed out of the target lexicon. The probable paths are evaluated by integrating character recognition scores with physical and spatial characteristics of the handwritten segments in a CRF (conditional random field) model and a beam search strategy is used to prune the set of likely paths. This approach has been benchmarked on the new IBM_UB_1 dataset as well as on the UNIPEN dataset for comparison.

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