Style Consistent Perturbation for Handwritten Chinese Character Recognition

Perturbation-based recognition is effective to recover the deformation of handwritten characters and improve the recognition performance by generating multiple distortions and selecting a distortion that best restores character deformation. Considering that the characters in a field undergo similar deformation under a consistent style, we proposed style consistent perturbation for handwritten character recognition. By generating multiple distortions for the characters in a field, each distortion style is evaluated at the field level and the uniform distortion style of maximum recognition confidence is selected to give the final result. To overcome the slight deviation from uniform style, we also propose to search the neighborhood distortions from the optimal uniform distortion for higher confidence. The experiments of handwritten Chinese character recognition on multi-writer data show that style consistent perturbation in very short fields outperforms individual character recognition, and neighborhood distortion search yields further improvement.