Automatic Quantitative Letter-level Extraction of Features Used by Document Examiners
Graham Leedham, Vladimir Pervouchine, Wei Kei Tan, A. C. Jacob · RUNE (Research UNE) · 2003
In this paper we examine tile automatic extraction of visual or structural features as used by document examiners in the comparison of handwriting samples. We have extracted between 7 and 14 different features from four letters ("y", "d", "f" and "t"). This analysis was earned out on a total of 3077 letters from 30 different writers. On average these features are extracted with about 88% accuracy and can be used to assess the similarity of different writing samples.