Stroke-guided pixel matching for handwritten Chinese character recognition

Structural matching and feature matching are two major streams in handwritten Chinese character recognition. The former is better in absorbing writing variation since it utilizes rich structural information from strokes directly. However, it is worse than the latter in handling noisy images, because of the difficulty in stroke extraction. For the ultimate performance in a recognition system, the structural information should be utilized without suffering from the faults of stroke extraction. This paper proposes a stroke-guided pixel matching method to satisfy the requirements. It uses a pixel-based character model to match images not depending on strokes. In order to utilize structural information, a stroke-guided pixel-grouping algorithm is also proposed. The initial experimental result shows that the proposed approach is promising