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

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.
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https://hal.inria.fr/inria-00112634
Contributor : Anne Jaigu <>
Submitted on : Thursday, November 9, 2006 - 1:56:26 PM
Last modification on : Thursday, November 9, 2006 - 4:44:29 PM
Long-term archiving on: : Tuesday, April 6, 2010 - 9:59:58 PM

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  • HAL Id : inria-00112634, version 1

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Kumar Chellapilla, Patrice Simard. A New Radical Based Approach to Offline Handwritten East-Asian Character Recognition. Tenth International Workshop on Frontiers in Handwriting Recognition, Université de Rennes 1, Oct 2006, La Baule (France). ⟨inria-00112634⟩

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