TY - GEN
T1 - Contrast enhancement of mail piece images
AU - Shin, Yong Chul
AU - Sridhar, Ramalingam
AU - Demjanenko, Victor
AU - Palumbo, Paul W.
AU - Hull, Jonathan J.
PY - 1992
Y1 - 1992
N2 - A New approach to contrast enhancement of mail piece images is presented. The contrast enhancement is used as a preprocessing step in the real-time address block location (RT-ABL) system. The RT-ABL system processes a stream of mail piece images and locates destination address blocks. Most of the mail pieces (classified into letters) show high contrast between background and foreground. As an extreme case, however, the seasonal greeting cards usually use colored envelopes which results in reduced contrast osured by an error rate by using a linear distributed associative memory (DAM). The DAM is trained to recognize the spectra of three classes of images: with high, medium, and low OCR error rates. The DAM is not forced to make a classification every time. It is allowed to reject as unknown a spectrum presented that does not closely resemble any that has been stored in the DAM. The DAM was fairly accurate with noisy images but conservative (i.e., rejected several text images as unknowns) when there was little ground and foreground degradations without affecting the nondegraded images. This approach provides local enhancement which adapts to local features. In order to simplify the computation of A and σ, dynamic programming technique is used. Implementation details, performance, and the results on test images are presented in this paper.
AB - A New approach to contrast enhancement of mail piece images is presented. The contrast enhancement is used as a preprocessing step in the real-time address block location (RT-ABL) system. The RT-ABL system processes a stream of mail piece images and locates destination address blocks. Most of the mail pieces (classified into letters) show high contrast between background and foreground. As an extreme case, however, the seasonal greeting cards usually use colored envelopes which results in reduced contrast osured by an error rate by using a linear distributed associative memory (DAM). The DAM is trained to recognize the spectra of three classes of images: with high, medium, and low OCR error rates. The DAM is not forced to make a classification every time. It is allowed to reject as unknown a spectrum presented that does not closely resemble any that has been stored in the DAM. The DAM was fairly accurate with noisy images but conservative (i.e., rejected several text images as unknowns) when there was little ground and foreground degradations without affecting the nondegraded images. This approach provides local enhancement which adapts to local features. In order to simplify the computation of A and σ, dynamic programming technique is used. Implementation details, performance, and the results on test images are presented in this paper.
UR - https://www.scopus.com/pages/publications/0027001111
M3 - Conference contribution
SN - 0819408158
T3 - Proceedings of SPIE - The International Society for Optical Engineering
SP - 27
EP - 37
BT - Proceedings of SPIE - The International Society for Optical Engineering
PB - Publ by Int Soc for Optical Engineering
T2 - Machine Vision Applications in Character Recognition and Industrial Inspection
Y2 - 10 February 1992 through 12 February 1992
ER -