Dynamics features Extraction for on-Line Signature verification
CONIELECOMP '04 Proceedings of the 14th International Conference on Electronics, Communications and Computers
Local Slant Estimation for Handwritten English Words
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
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Designing the algorithm to extract features in signature is a challenging task due to complex human behavior which is developed through repetition. Features such as direction, slant, baseline, pressure, speed and numbers of pen ups and downs are some of the dynamic information signature that can be extracted from an online method. However, the variables for identifying the features is rarely discussed and notified. Therefore, this paper presents the variables that involve in designing the algorithm for extracting the slant features. Both local and global slant characteristics are considered in extracting the features. Local slant is the longest slant among the detected slant while the global slant represents the highest quantity of classified slant whether the slant are leftward, upright or rightward. Signature Extraction Features System (SEFS) is used to extract the slant features in signature automatically for analysis purposes. The images created by SEFS are used as samples for the questionnaire to identify the slant features and to be given to human expert for evaluation. The results from the SEFS are compared with the result from the questionnaire. The results demonstrate a competitive performance with 85% accuracy.