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14 April 1993 Enhancement of mail piece images based on window statistics
Yong-Chul Shin, Ramalingam Sridhar, Sargur N. Srihari
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Proceedings Volume 1906, Character Recognition Technologies; (1993) https://doi.org/10.1117/12.143634
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1993, San Jose, CA, United States
Abstract
An image enhancement technique for mail piece images based on window statistics is presented. The approach has been developed to increase the image quality for the subsequent segmentation and block analysis in the real-time address block location (RT-ABL) system that processes a stream of mail piece images and locates the destination address block. As a framework of this approach, window statistics consisting of local average, (Alpha) , local standard deviation, (sigma) , and center pixel value, (Rho) , over a 9 X 9 window are used. This approach includes contrast enhancement, bleed-through removal, and binarization. Contrast enhancement and bleed-through removal are achieved through a nonlinear mapping M((Alpha) , (sigma) , (Rho) ) obtained empirically. A simple and efficient binarization is also obtained using the ratio of a pixel value of gray scale output (Rho) ' obtained from the mapping and (Alpha) . Major advantages of this method are the avoidance of black-out or white-out that are encountered in other binarization methods on low contrast images, and improved character segmentation that helps the segmentation tool to locate key components of the destination address block, such as state abbreviation or ZIP Code. Examples of images transformed using the method are presented along with a discussion of the performance comparisons.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Yong-Chul Shin, Ramalingam Sridhar, and Sargur N. Srihari "Enhancement of mail piece images based on window statistics", Proc. SPIE 1906, Character Recognition Technologies, (14 April 1993); https://doi.org/10.1117/12.143634
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KEYWORDS
Image segmentation
Image processing
Image enhancement
Optical character recognition
Binary data
Image processing algorithms and systems
Image contrast enhancement
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