A New Radical Based Approach to Offline Handwritten East-Asian Character RecognitionReport as inadecuate

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1 Microsoft Research

Abstract : 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.

Keywords : East-Asian handwritten character recognition radicals convolutional neural networks

Author: Kumar Chellapilla - Patrice Simard -

Source: https://hal.archives-ouvertes.fr/


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