DocumentCode :
3487779
Title :
Extraction of Serial Numbers on Bank Notes
Author :
Bo-Yuan Feng ; Mingwu Ren ; Xu-Yao Zhang ; Suen, Ching
Author_Institution :
Dept. of Comput. Sci., Nanjing Univ. of Sci. & Technol., Nanjing, China
fYear :
2013
fDate :
25-28 Aug. 2013
Firstpage :
698
Lastpage :
702
Abstract :
The study of RMB (renminbi bank note, the paper currency used in China) serial number recognition draws more and more attention in recent years, for reducing financial crime, improving financial market stability and social security. The accuracy of RMB recognition relies heavily on the extraction, which is a challenging problem due to background variations and uneven illumination. In this paper, we present a new system that extracts the RMB characters directly from scanned RMB images. First, two different techniques, namely skew correction and orientation identification are used to detect the region which contains RMB serial number. Then the detected text region is binarized by a combined thresholding technique. After that, a local contrast average method is introduced to extract the RMB characters from the binarization result. The experiments demonstrate that the proposed binarization method outperforms other well-known methods. For character extraction, we report an overlap-recall rate of 79.68% and an overlap-precision rate of 98.10% respectively.
Keywords :
banking; feature extraction; image segmentation; object recognition; stock markets; text detection; China; RMB character extraction; RMB recognition; binarization method; financial crime reduction; financial market stability; local contrast average method; orientation identification; overlap-precision rate; overlap-recall rate; paper currency; renminbi bank note; serial number extraction; serial number recognition; skew correction; social security; text region detection; thresholding technique; Image edge detection; Lighting; Measurement; Noise; Security; Text analysis; RMB serial number extraction; combination technique; image binarization; local contrast;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis and Recognition (ICDAR), 2013 12th International Conference on
Conference_Location :
Washington, DC
ISSN :
1520-5363
Type :
conf
DOI :
10.1109/ICDAR.2013.143
Filename :
6628708
Link To Document :
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