Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6043
Title: Length estimation of digit strings using a neural network with structure-based features
Authors: Lu, Z
Chi, ZG 
Siu, WC 
Issue Date: Jan-1998
Source: Journal of electronic imaging, Jan. 1998, v. 7, no. 1, p. 79-85
Abstract: Accurate length estimation is very helpful for the successful segmentation and recognition of connected digit strings, in particular, for an off-line recognition system. However, little work has been done in this area due to the difficulties involved. A length estimation approach is presented as a part of our automatic off-line digit recognition system. The kernel of our approach is a neural network estimator with a set of structure-based features as the inputs. The system outputs are a set of fuzzy membership grades reflecting the degrees of an input digit string of having different lengths. Experimental results on National Institute of Standards and Technology (NIST) Special Database 3 and other derived digit strings shows that our approach can achieve an about 99.4% correct estimation if the best two estimations are considered.
Keywords: Feature extraction
Feedforward neural nets
Fuzzy neural nets
Image recognition
Image segmentation
Multilayer perceptrons
Parameter estimation
Publisher: SPIE-International Society for Optical Engineering
Journal: Journal of electronic imaging 
ISSN: 1017-9909
EISSN: 1560-229X
DOI: 10.1117/1.482629
Rights: Lu Z, Chi Z and Siu W, "Length estimation of digit strings using a neural network with structure-based features," J. Electron. Imaging., 7(1), p. 79-85 (1998)
Copyright 1998 Society of Photo-Optical Instrumentation Engineers & Society for Imaging Science and Technology. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
http://dx.doi.org/10.1117/1.482629
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