Chen-Yong Cher, Michael Gschwind
VEE 2008
In this paper, we describe a system for rapid verification of unconstrained off-line handwritten phrases using perceptual holistic features of the handwritten phrase image. The system is used to verify handwritten street names automatically extracted from live U.S. mail against recognition results of analytical classifiers. Presented with a binary image of a street name and an ASCII street name, holistic features (reference lines, large gaps and local contour extrema) of the street name hypothesis are 'predicted' from the expected features of the constituent characters using heuristic rules. A dynamic programming algorithm is used to match the predicted features with the extracted image features. Classes of holistic features are matched sequentially in increasing order of cost, allowing an ACCEPT/REJECT decision to be arrived at in a time-efficient manner. The system rejects errors with 98 percent accuracy at the 30 percent accept level, while consuming approximately 20/msec per image on the average on a 150 MHz SPARC 10.
Chen-Yong Cher, Michael Gschwind
VEE 2008
P.C. Yue, C.K. Wong
Journal of the ACM
Berrin Yanikoglu, Peter A. Sandon
Pattern Recognition
Rina Dechter, Kalev Kask, et al.
AAAI/IAAI 2002