GMM-based handwriting style identification system for historical documents
Fouad Slimane|Torsten Schaban|Volker Märgner
In this paper, we describe a novel method for handwriting style identification. A can be common to one or several writer. It represent also used in period of the history specific document. Our is based on Gaussian Mixture Models (GMMs) using different kind features computed combined fixed-length horizontal and vertical sliding window moving over document page. For each writing GMM built trained page images. At recognition phase, system returns log-likelihood scores. The model with highest score selected. Experiments images from historical German collection demonstrate good performance results. identification rate GMM-based developed six 100%.
Infoscience (Ecole Polytechnique Fédérale de Lausanne)