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has been extensively studied in the past decades, but there are 
still several possibilities for improvement in the field. To 
achieve better verification results, we propose a simplified 
probabilistic model for off-line signature verification. In our 
model, each of the verification steps can be mathematically 
described and, therefore, individually analyzed and improved. 
Using this model, we are able to predict the accuracy of a 
signature verification system based on just a few a priori known 
parameters, such as the cardinality and the quality of input 
samples. Several experiments have been conducted using our 
statistics-based classifier to confirm the assumptions and the 
results of our model. Based on the results, we can provide 
answers to several old questions within the field, such as why 
is it so hard to achieve error rates below 10% or how does the 
number of original samples and features affect the final error 
rates.</abstractText>
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      <label>Kovari Bence et al. A study on the consistency and significance of local features in off-line signature verification. 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