An off-line cursive handwriting recognition system

Author

Senior, Andrew W. ; Robinson, Anthony J.

Author_Institution

IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA

Volume

20

Issue

3

fYear

1998

fDate

3/1/1998 12:00:00 AM

Firstpage

309

Lastpage

321

Abstract

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

Keywords

feature extraction; handwriting recognition; hidden Markov models; optical character recognition; recurrent neural nets; hidden Markov model; normalization; off-line cursive handwriting recognition system; out-of-vocabulary word recognition; preprocessing techniques; rejection; scale invariance; segmentation; skeleton; slant invariance; slope invariance; stroke features; stroke thickness invariance; vocabulary choice; word images; Handwriting recognition; Hidden Markov models; Image segmentation; Optical character recognition software; Optical computing; Optical fiber networks; Postal services; Recurrent neural networks; Skeleton; Vocabulary;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.667887

Filename

667887