GMM-based Handwriting Style Identification System for Historical Documents

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


Published in:
Proceedings of the 6th International Conference of Soft Computing and Pattern Recognition, 387-392
Presented at:
6th International Conference of Soft Computing and Pattern Recognition, Tunis, Tunisia, August 11-14, 2014
Year:
2014
Publisher:
Tunis, Tunisia
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 Record created 2014-08-18, last modified 2018-03-17

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