Combining Multiple Representations for Pen-based Handwritten Digit Recognition

Fevzi ALİMOĞLU; Ethem ALPAYDIN

Article Details

Combining Multiple Representations for Pen-based Handwritten Digit Recognition

Authors:

Fevzi ALİMOĞLU | Ethem ALPAYDIN

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2001 Vol.9 Issue 1, pp.1-12

Document Type:

Article

Publication Date:

12/2000

Document Language:

Turkish

Document ID (Org No.):

2000744384

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632

Abstract:

 

We investigate techniques to combine multiple representations of a  handwritten digit to increase classification accuracy without  significantly increasing system complexity or recognition time.  In  pen-based recognition, the input is the dynamic movement of the pentip  over the pressure sensitive tablet.  There is also the image formed as  a result of this movement.  On a real-world database of handwritten  digits containing more than 11,000 handwritten digits, we notice that  the two multi-layer perceptron (MLP) based classifiers using these  representations make errors on different patterns implying that a  suitable combination of the two would lead to higher accuracy.  We  implement and compare voting, mixture of experts, stacking and  cascading.  Combining the two MLP classifiers we indeed get higher  accuracy because the two classifiers/representations fail on different  patterns.  We especially advocate multistage cascading scheme where  the second costlier image-based classifier is employed only in a small  percentage of cases.

 

We investigate techniques to combine multiple representations of a  handwritten digit to increase classification accuracy without  significantly increasing system complexity or recognition time.  In  pen-based recognition, the input is the dynamic movement of the pentip  over the pressure sensitive tablet.  There is also the image formed as  a result of this movement.  On a real-world database of handwritten  digits containing more than 11,000 handwritten digits, we notice that  the two multi-layer perceptron (MLP) based classifiers using these  representations make errors on different patterns implying that a  suitable combination of the two would lead to higher accuracy.  We  implement and compare voting, mixture of experts, stacking and  cascading.  Combining the two MLP classifiers we indeed get higher  accuracy because the two classifiers/representations fail on different  patterns.  We especially advocate multistage cascading scheme where  the second costlier image-based classifier is employed only in a small  percentage of cases.

Article Details

Combining Multiple Representations for Pen-based Handwritten Digit Recognition

Authors: Fevzi ALİMOĞLU | Ethem ALPAYDIN

Publisher: TUBITAK

Journal: Turkish Journal of Electrical Engineering and Computer Science 2001 Vol.9 Issue 1, pp.1-12

Document Type: Article

Publication Date: 12/2000

Document Language: Turkish

Document ID (Org No.): 2000744384

Frequency: Bimonthly

eISSN: 1303-6203

pISSN: 1300-0632

Issues

Authors:

Fevzi ALİMOĞLU | Ethem ALPAYDIN

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2001 Vol.9 Issue 1, pp.1-12

Document Type:

Article

Publication Date:

12/2000

Document Language:

Turkish

Document ID (Org No.):

2000744384

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632