Defining Writer's Invariants to Adapt the Recognition Task
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
A set of handwriting families: style recognition
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Ink Texture Analysis for Writer Identification
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Writers Authentication and Fractal Compression
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Verification of dynamic curves extracted from static handwritten scripts
Pattern Recognition
Texture analysis for stroke classification in infrared reflectogramms
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Handwritten signature identification using basic concepts of graph theory
WSEAS Transactions on Signal Processing
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When identifying a writer from a handwritten text, mostoften, either some characteristic patterns or some shapeparameters are extracted. They are assumed to be specificof the writer. Here, we are to explore a differentapproach, we consider the distribution of the pixel graylevels within the line. It is linked to pressure and writingspeed when text is realized.In the line, the direction that is perpendicular to thewriting way of drawing is privileged. The curve associatedwith the gray levels in a stroke section is characterized byuse of 4 shape parameters. More over the regular sectionsare selected and are grouped in section lots. Thedistributions of the sections and of the section lots arequantified. Thus 22 parameters are extracted. Threedifferent classifiers are used with and without geneticselection of the most significant parameters for theclassifier. Then the classifiers are combined and theresults show the gray level distribution within the writingis characterizing the writer in a significant way.