A fuzzy image congealing-based handwritten Chinese character recognition and classification system

The recognition and classification of handwritten Chinese characters poses a significant challenge for automated methods. Indeed the sheer number of characters, intricate complexity of such characters, and variations in writing styles mean that the task can be difficult even for humans. Previous work in this area has focused upon methods which perform a certain form of feature extraction and segmentation as the basis for building systems to perform this task. This paper proposes two approaches for handwritten Chinese character recognition and classification using an image alignment technique based on a fuzzy-entropy metric. Rather than extracting features from the image, which can often result in subjective and poorly-fitting models, the proposed methods instead uses the mean image transformations of the training phase as a basis for building models. The use of a fuzzy-entropy based metric also means improved ability to model different types of uncertainty. The mean image transformations are then collated, and used as training data to classify the images of test characters. A nearest-neighbour classifier based on Euclidean distance is then used to classify each test character. The approaches are applied to a publicly available real-world database of handwritten Chinese characters and demonstrate that they can achieve high classification accuracy.