Off-line writer verification using segments of handwritten samples and SVM
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This works presents a method to verify a person identity based on off-line handwritten strokes analysis. Its main contribution is that the descriptors are obtained from the constitutive segments of each grapheme, in contrast with the complexity of the handwritten images used in signature recognition system or even with the graphemes themselves. In this way, only few handwriting samples taken from a short text could be enough to identify the writer. The descriptor is based on an estimation of the pressure of the stroke grayscale image. In particular, the average of the gray levels on the perpendicular line to the skeleton is used. A semi-automatic procedure is used to extract the segments from scanned images. The repository consists of 3.000 images of 6 different segments. Binary-output Support Vector Machine classifiers are used. Two types of cross validation, K-fold and Leave-one-out, are implemented to objectively evaluate the descriptor performance. The results are encouraging. A hit rate of 98% in identity verification is obtained for the 6 segments studied.
FuenteAdvances in Intelligent Systems and Computing, 1267
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