Title :
Writer identification based on graphology techniques
Author :
Santana, Omar ; Travieso, Carlos M. ; Alonso, Jesús B. ; Ferrer, Miguel A.
Author_Institution :
Dipt. de Senales y Comun., Univ. de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria
Abstract :
In this work an innovative system biometric of writerspsila identification based in technical expert calligraphic and graphology on the handwritten script is presented. It has been developed working in off-line mode on a Spanish words image database, formed by 29 different individuals. All the extractions of characteristics carried out on the images and that they have been used for the identification, they were carried out by means of the estimate of several elements object using studies of The French Graphology School, and that they are commonly employed by the handwriting experts in judicial matters. The success percentage achieved with 5 of these characteristics from this database of 29 writers is of 99.34%. In new experimentations, with these same parameters and enlarging the database to 70 users, a rate of success of 92% was reached.
Keywords :
handwritten character recognition; neural nets; support vector machines; Spanish word image database; artificial neural network; biometric identification; graphology technique; handwritten script; handwritten writer recognition; support vector machine; technical expert calligraphic; Artificial neural networks; Biometrics; Handwriting recognition; Image databases; Law; Pins; Robustness; Security; Support vector machines; Writing; Artificial Neural Networks (NNs) and Support Vector Machines (SVMs); Biometric Identification; Graphology Techniques; Writer Identification;
Conference_Titel :
Security Technology, 2008. ICCST 2008. 42nd Annual IEEE International Carnahan Conference on
Conference_Location :
Prague
Print_ISBN :
978-1-4244-1816-9
Electronic_ISBN :
978-1-4244-1817-6
DOI :
10.1109/CCST.2008.4751297
