Word normalization for online handwritten word recognition

Author

Bengio, Yoshua ; Le Cun, Yann

Author_Institution

Dept. d´´Inf. et de Recherche Oper., Montreal Univ., Que., Canada

Volume

2

fYear

1994

fDate

9-13 Oct 1994

Firstpage

409

Abstract

We introduce a new approach to normalizing words written with an electronic stylus that applies to all styles of handwriting (upper case, lower case, printed, cursive, or mixed). A geometrical model of the word spatial structure is fitted to the pen trajectory using the expectation-maximisation algorithm. The fitting process maximizes the likelihood of the trajectory given the model and a set a priors on its parameters. The method was evaluated and integrated to a recognition system that combines neural networks and hidden Markov models

Keywords

character recognition; electronic stylus; expectation-maximisation algorithm; fitting process; geometrical model; hidden Markov models; neural networks; online handwritten word recognition; pen trajectory; trajectory maximum likelihood; word normalization; word spatial structure; Character recognition; Delay; Face recognition; Handwriting recognition; Hidden Markov models; Image recognition; Neural networks; Performance evaluation; Shape; Solid modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1994. Vol. 2 - Conference B: Computer Vision & Image Processing., Proceedings of the 12th IAPR International. Conference on

Conference_Location

Jerusalem

Print_ISBN

0-8186-6270-0

Type

conf

DOI

10.1109/ICPR.1994.576966

Filename

576966