Size normalization in on-line unconstrained handwriting recognition

Abstract

In an on-line handwriting recognition system, the motion of the tip of the stylus (pen) is sampled at equal time intervals using a digitizer tablet and the sampled points are passed to a computer which performs the handwriting recognition. In most cases, the basic recognition algorithm performs best for a nominal size of writing as well as a standard orientation (normally horizontal) and a nominal slant (normally fully upright). We discuss and provide solutions to these normalization problems in the context of on-line handwriting recognition. Most of the results presented are also valid for optical character recognition (OCR). Error rate reductions of 54.3% and 35.8% were obtained for the writer-dependent and writer-independent samples through using the proposed normalization scheme.