A Component-Based On-Line Handwritten Tibetan Character Recognition Method Using Conditional Random Field

This paper presents a new component-based recognition method using conditional random field (CRF) for on-line handwritten Tibetan characters. The character pattern is over-segmented into a sequence of sub-structure blocks. Integrated segmentation and recognition method based on the CRF model is used to determine the component segmentation points from these block sequences. The CRF model combines component shape likelihood with geometrical likelihood. The parameters are learned using an energy minimization method. We build a component-based spelling rule model to ensure the correct component appearing at a specific structural position. A character-component generation model is presented to reduce component recognition error rate and accelerate the recognition process. Experimental results on MRG-OHTC database show that the proposed method gives promising performance comparing with the holistic method and the component-based conventional path evaluation method.