Similar Handwritten Chinese Character Recognition Using Hierarchical CNN Model
We propose a hierarchical CNN model for the recognition of confusable similar handwritten Chinese characters, which are automatically extracted from a large character set by utilizing a classifier's recognition result. The proposed hierarchical CNN model takes advantage of deep networks and traditional hierarchical methods, and consists of two stages, which are expected to differentiate inter-group characters and intra-group characters, respectively. Different from traditional ways of expanding depth and/or width of general sole classifier CNNs, we explore the way of designing multiple parallel CNN classifiers to capture critical regions of similar characters. Each classifier along with their feature extraction layers is trained only with a group of similar characters so that the subtle shape difference can be captured. Totally, 368 similar characters (categorized into 172 groups) are extracted from 3755 frequently used Chinese characters. Experimental results on these similar characters demonstrate the superiority of the proposed method to the expanded CNN models.