An off-line cursive handwriting recognition system

Describes a complete system for the recognition of off-line handwriting. Preprocessing techniques are described, including segmentation and normalization of word images to give invariance to scale, slant, slope and stroke thickness. Representation of the image is discussed and the skeleton and strok...

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Bibliographic Details
Published inIEEE transactions on pattern analysis and machine intelligence Vol. 20; no. 3; pp. 309 - 321
Main Authors Senior, A.W., Robinson, A.J.
Format Journal Article
LanguageEnglish
Published IEEE 01.03.1998
Subjects
Handwriting recognition
Hidden Markov models
Image segmentation
Optical character recognition software
Optical computing
Optical fiber networks
Postal services
Recurrent neural networks
Skeleton
Vocabulary
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Summary:Describes a complete system for the recognition of off-line handwriting. Preprocessing techniques are described, including segmentation and normalization of word images to give invariance to scale, slant, slope and stroke thickness. Representation of the image is discussed and the skeleton and stroke features used are described. A recurrent neural network is used to estimate probabilities for the characters represented in the skeleton. The operation of the hidden Markov model that calculates the best word in the lexicon is also described. Issues of vocabulary choice, rejection, and out-of-vocabulary word recognition are discussed.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0162-8828
1939-3539
DOI:10.1109/34.667887