Deep Neural Network with a Characteristic Analysis for Seal Stroke Recognition

Abstract

Seal characters are derived from ancient Chinese pictographs, naturally inheriting pictographic characteristics and complex structures. As the essential components of seal characters, seal strokes play a vital role in seal character recognition, composition and writing, so accurate recognition of seal strokes can greatly promote the investigation of seal characters. Inspired by curve fitting, we propose a new model called the characteristic analysis neural network (CANN) for seal stroke recognition. Instead of indiscriminate grasping of feature information in regular neural networks, we design an efficient approximation technique based on the piecewise Bezier curves that can effectively facilitate structural compression and lossless feature extraction. The feature extraction capability of Bezier approximation helps the methodology achieve impressive recognition accuracy not only on the seal strokes but also on any curve-based symbols. Furthermore, the hierarchical structure of the deep learning strategy is inherited and improved for better performance with high generalisation. Experiments conducted on different types of strokes verify that CANN obtains superior performance on both seal strokes and other smooth symbols. The robustness and the effectiveness of CANN are also demonstrated with minimal learning cost compared to other state-of-art models.