NDLI: Industrial character recognition based on grid feature and wavelet moment

Content Provider	IEEE Xplore Digital Library
Author	Yong Zhang Sanxia Xie Shu Wei
Copyright Year	2013
Description	Author affiliation: Sch. of Instrum. Sci. & Opto-Electron. Eng., Beihang Univ., Beijing, China (Yong Zhang; Sanxia Xie; Shu Wei)
Abstract	A novel descriptor for laser printing character recognition is proposed by using the fusion features and multilevel classification. There are two level features in the feature extraction module, the first level feature uses the coarse grid statistical features, having great applicability for local character distortion and stroke thickness inequality. Wavelet moment, as the second level feature, has the scale, translation and rotation invariance and the great anti-noise capability. It can reflect the approximation of the overall character and local detail information at the same time, so it is suitable for classing similar characters. As for the classification module, based on results of primary feature template matching classification in the first place, the second matching uses two level features fusion and special distortion character template to classify the top three character in the results. Experimental results demonstrate that using the fusion features and multilevel classification, industrial laser character recognition rate can be up to 99.2%, which is better than that of using single stage feature recognition.
Sponsorship	IEEE Instrum. Meas. Soc.
Starting Page	56
Ending Page	59
File Size	146710
Page Count	4
File Format	PDF
e-ISBN	9781467357913
DOI	10.1109/IST.2013.6729662
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Character recognition Wavelet transforms Shape Image recognition Classification algorithms Two Level Matching Laser Printing Characters Recognition Wavelet Moment Feature Fusion
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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