Multiple novelty input neural networks for unconstrained handwritten numeral recognition

Kil Taek Lim; Sung Il Chien; Soon Ju Kang

doi:10.1049/el:19980769

Multiple novelty input neural networks for unconstrained handwritten numeral recognition

Kil Taek Lim
, Sung Il Chien
, Soon Ju Kang

Kyungpook National University

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Multiple novelty input neural networks have been presented. It is shown that this method produces relatively good recognition accuracy for an unconstrained handwritten numeral database.

Original language	English
Pages (from-to)	1112-1113
Number of pages	2
Journal	Electronics Letters
Volume	34
Issue number	11
DOIs	https://doi.org/10.1049/el:19980769
State	Published - 28 May 1998

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title = "Multiple novelty input neural networks for unconstrained handwritten numeral recognition",

abstract = "Multiple novelty input neural networks have been presented. It is shown that this method produces relatively good recognition accuracy for an unconstrained handwritten numeral database.",

author = "Lim, \{Kil Taek\} and Chien, \{Sung Il\} and Kang, \{Soon Ju\}",

year = "1998",

month = may,

day = "28",

doi = "10.1049/el:19980769",

language = "English",

volume = "34",

pages = "1112--1113",

journal = "Electronics Letters",

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T1 - Multiple novelty input neural networks for unconstrained handwritten numeral recognition

AU - Lim, Kil Taek

AU - Chien, Sung Il

AU - Kang, Soon Ju

PY - 1998/5/28

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N2 - Multiple novelty input neural networks have been presented. It is shown that this method produces relatively good recognition accuracy for an unconstrained handwritten numeral database.

AB - Multiple novelty input neural networks have been presented. It is shown that this method produces relatively good recognition accuracy for an unconstrained handwritten numeral database.

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DO - 10.1049/el:19980769

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SN - 0013-5194

VL - 34

SP - 1112

EP - 1113

JO - Electronics Letters

JF - Electronics Letters

IS - 11

ER -

Multiple novelty input neural networks for unconstrained handwritten numeral recognition

Abstract

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