A structural deformable model with application to post-recognition of handwritings

It is observed that most of the existing deformable models (DMs) do not incorporate structural information into the model and can merely deform according to the spatial relationship between primitives. Structural information, which is essential in various pattern recognition tasks, has been ignored usually. In this paper, we address this issue by proposing a new class of DMs called structural deformable model (SDM) which is capable to modeling the complex structure of patterns and is able to deform in a well-controlled manner. The new model takes structural information into accounts by representing a pattern as a hierarchy of components, namely, image, objects, snakes, segments and snaxels that are structurally connected with each others. It deforms by minimizing the distortion of its inter-object and intra-object structure while matching with the target patterns. A smoothing scheme is introduced to achieve coarse-to-fine matching, making the deformation process behaved in a desirable way. The effectiveness of the proposed model has been demonstrated through various experiments in Chinese character recognition, which is well known for its highly structural patterns.