A Study of Designing Compact Recognizers of Handwritten Chinese Characters Using Multiple-Prototype Based Classifiers

We present a study of designing compact recognizers of handwritten Chinese characters using multiple-prototype based classifiers. A modified Quick prop algorithm is proposed to optimize a sample-separation-margin based minimum classification error objective function. Split vector quantization technique is used to compress classifier parameters. Benchmark results are reported for classifiers with different footprints trained from about 10 million samples on a recognition task with a vocabulary of 9282 character classes which include 9119 Chinese characters, 62 alphanumeric characters, 101 punctuation marks and symbols.