DocumentCode
3260189
Title
Hand written letter recognition with neural networks
Author
Lee, H.Y. ; Lee, Y.C. ; Chen, He Henry
Author_Institution
Maryland Univ., College Park, MD, USA
fYear
1989
fDate
0-0 1989
Abstract
Summary form only given, as follows. Neural networks that can recognize 36 handwritten alphanumeric characters are studied. Thin line letters, in 32*32 binary arrays, are used as the input pattern. The system is built from two major units, a three-layered preprocessing unit and a recognition unit. Shift, scale, and deformation tolerance in recognition are provided through reprocessing. Three learning paradigms including an error backpropagation learning, a simple perceptron learning, and a competitive learning are examined and compared.<>
Keywords
character recognition; learning systems; neural nets; binary arrays; competitive learning; deformation tolerance; error backpropagation learning; handwritten alphanumeric characters; input pattern; learning paradigms; letter recognition; neural networks; perceptron learning; recognition unit; reprocessing; three-layered preprocessing unit; Character recognition; Learning systems; Neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location
Washington, DC, USA
Type
conf
DOI
10.1109/IJCNN.1989.118463
Filename
118463
Link To Document