A scanning n-tuple classifier for online recognition of handwritten digits

Author

Ratzlaff, Eugene H.

Author_Institution

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

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

18

Lastpage

22

Abstract

A scanning n-tuple classifier is applied to the task of recognizing online handwritten isolated digits. Various aspects of preprocessing, feature extraction, training and application of the scanning n-tuple method are examined. These include: distortion transformations of training data, test data perturbations, variations in bitmap generation and scaling, chain code extraction and concatenation, various static and dynamic features, and scanning n-tuple combinations. Results are reported for both the UNIPEN Train-R01/V07 and DevTest-R01/V02 subset la isolated digits databases

Keywords

convolution; feature extraction; handwritten character recognition; learning (artificial intelligence); pattern classification; bitmap generation; convolution; feature extraction; handwritten digit recognition; preprocessing; scaling; scanning n-tuple classifier; Character recognition; Data mining; Feature extraction; Handwriting recognition; Image generation; Image sampling; Smoothing methods; Spatial databases; Testing; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on

Conference_Location

Seattle, WA

Print_ISBN

0-7695-1263-1

Type

conf

DOI

10.1109/ICDAR.2001.953747

Filename

953747