A New Radical Based Approach to Offline Handwritten East-Asian Character Recognition

East-Asian characters possess a rich hierarchical structure with each character comprising a unique spatial arrangement of radicals (sub-characters). In this paper, we present a new radical based approach for scaling neural network (NN) recognizers to thousands of East-Asian characters. The proposed off-line character recognizer comprises neural networks arranged in a graph. Each NN is one of three types: a radical-at-location (RAL) recognizer, a gater, or a combiner. Each radical-atlocation NN is a convolutional neural network that is designed to processes the whole character image and recognize radicals at a specific location in the character. Example locations include left-half, right-half, top-half, bottom-half, left-top quadrant, bottom-right quadrant, etc. Segmentation is completely avoided by allowing each RAL classifier to process the whole character image. Gater-NNs reduce the number of NNs that need to be evaluated at runtime and combiner-NNs combine RAL classifier outputs for final recognition. The proposed approach is tested on a real-world dataset containing 13.4 million handwritten Chinese character samples from 3665 classes. Experimental results indicate that the proposed approach scales well and achieves a low error rate.