Title :
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
fDate :
6/23/1905 12:00:00 AM
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;
Conference_Titel :
Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7695-1263-1
DOI :
10.1109/ICDAR.2001.953747
