Automatic generation of nearly optimal decision trees for handwritten character recognition

• Autorzy: Szkodzik A. , Materka A.
• Języki publikacji: EN

Abstrakty

EN

This paper present results of applying decision tree to printed and handwritten character recognition. An automatic feature generation method was employed during the construction of the tree, which improved the recognition rate for the testing set. This learning technique significantly reduces the drawback of the tree classifiers that is thier rapid error accumulation with depth, while it does not influence the size of trees. It was shown that the proposed approach gives better results thsn increasing the size of the training sets used for construction of the trees. The recognition rate above 97% was obtained by means of a parallel classifier built of multiple decision trees despite no advanced preprocessing of input characters (like skeletonization or slant reduction) was performed.

Słowa kluczowe

EN

optical character recognition; automatic feature generation; feature extraction; decision tree; parallel classifier

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 1999
• Tom: Vol. 8, No. 1
• Strony: 117--128
• Opis fizyczny: Bibliogr. 10 poz.

Twórcy

autor

Szkodzik A.

autor

Materka A.

• Institute of Electronics, Technical University of Łódź, Stefanowskiego 18/22, 90-924 Łódź, Poland