Optimal design of reference models for large-set handwritten character recognition

For the recognition of large-set handwritten characters, classification methods based on pattern matching have been commonly used, and good reference models play an important role in achieving high performance in these methods. Learning Vector Quantization (LVQ) has been intensively studied to generate good reference models in speech recognition since 1986. However, the design of reference models based on LVQ has several drawbacks for the recognition of large-set handwritten characters. In this paper, to cope with these, we propose a new method for the optimal design of reference models using Simulated Annealing combined with an improved LVQ3 for the recognition of large-set handwritten characters. Experimental results reveal that the proposed method is superior to the conventional method based on averaging or other LVQ-based methods.