Connected components in binary images: the detection problem
Connected components in binary images: the detection problem
Real time character scaling and rotation
ACM SIGAPP Applied Computing Review - Special issue on security
DL '97 Proceedings of the second ACM international conference on Digital libraries
TextFinder: An Automatic System to Detect and Recognize Text In Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Optimization Methodology for Document Structure Extraction on Latin Character Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Postal address block location by contour clustering
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
An Image-based Mail Facing and Orientation System for Enhanced Postal Automation
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A fast algorithm of address lines extraction on complex Chinese mail pieces
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
| Hi-index | 4.10 |
The CEDAR real-time address block location system, which determines candidates for the location of the destination address from a scanned mail piece image, is described. For each candidate destination address block (DAB), the address block location (ABL) system determines the line segmentation, global orientation, block skew, an indication of whether the address appears to be handwritten or machine printed, and a value indicating the degree of confidence that the block actually contains the destination address. With 20-MHz Sparc processors, the average time per mail piece for the combined hardware and software system components is 0.210 seconds. The system located 89.0% of the addresses as the top choice. Recent developments in the system include the use of a top-down segmentation tool, address syntax analysis using only connected component data, and improvements to the segmentation refinement routines. This has increased top choice performance to 91.4%.