Statistical Pattern Recognition for Automatic Writer Identification and Verification
نویسنده
چکیده
This chapter evaluates the performance of edge-based directional probability distributions as features in writer identification in comparison to a number of other texture-level features encoding non-angular information. We introduce here a new feature: the joint probability distribution of the angle combination of two ”hinged” edge fragments. It is noted that the ”edge-hinge” distribution outperforms all other individual features. Combining features yields improved performance. Limitations of the studied global features pertain to the amount of handwritten material needed in order to obtain reliable distribution estimates. A stability test is carried out showing the dependence of writer identification accuracy on the amount of handwritten material used for feature extraction.
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