Length estimation of digit strings using a neural network with structure-based features

Zhongkang Lu, Zheru Chi, Wan Chi Siu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)79-85
Number of pages7
JournalJournal of Electronic Imaging
Volume7
Issue number1
DOIs
Publication statusPublished - 1 Jan 1998

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Computer Science Applications
  • Electrical and Electronic Engineering

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