TY - GEN
T1 - A sigma-lognormal model for handwritten text CAPTCHA generation
AU - Ramaiah, Chetan
AU - Plamondonm, Rejean
AU - Govindaraju, Venu
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/4
Y1 - 2014/12/4
N2 - Popular CAPTCHA systems consist of garbled printed text character images with significant distortions and noise. It is believed that humans have little difficulty in deciphering the text, whereas automated systems are foiled by the added noise and distortion. However, in recent years, several text based CAPTCHAs have been reported as broken, that is, automated systems can identify the text in the displayed image with a reasonable amount of success. An extension to the text based CAPTCHA concept is to utilize unconstrained handwritten text, which is still considered to be a challenging problem for automated systems. In this work, we present a automated handwritten CAPTCHA generation system by adding distortions to the Sigma-Lognormal representation of a handwritten word sample. In addition, several noise models are also considered. We perform experiments on the UNIPEN dataset and demonstrate the efficacy of the approach.
AB - Popular CAPTCHA systems consist of garbled printed text character images with significant distortions and noise. It is believed that humans have little difficulty in deciphering the text, whereas automated systems are foiled by the added noise and distortion. However, in recent years, several text based CAPTCHAs have been reported as broken, that is, automated systems can identify the text in the displayed image with a reasonable amount of success. An extension to the text based CAPTCHA concept is to utilize unconstrained handwritten text, which is still considered to be a challenging problem for automated systems. In this work, we present a automated handwritten CAPTCHA generation system by adding distortions to the Sigma-Lognormal representation of a handwritten word sample. In addition, several noise models are also considered. We perform experiments on the UNIPEN dataset and demonstrate the efficacy of the approach.
UR - https://www.scopus.com/pages/publications/84919909254
U2 - 10.1109/ICPR.2014.52
DO - 10.1109/ICPR.2014.52
M3 - Conference contribution
AN - SCOPUS:84919909254
T3 - Proceedings - International Conference on Pattern Recognition
SP - 250
EP - 255
BT - Proceedings - International Conference on Pattern Recognition
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Conference on Pattern Recognition, ICPR 2014
Y2 - 24 August 2014 through 28 August 2014
ER -