Title :
A septon feature scheme in handwritten digit recognition
Author :
Agba, Lawrence C. ; Shankar, Ravi
Author_Institution :
Div. of Sci. & Math., Bethune-Cookman Coll., Daytona Beach, FL, USA
Abstract :
A set of comprehensive features suitable for character recognition is proposed. The features are extracted from thinned binary images and classified using an artificial neural network. A special application of this septon feature extraction scheme in handwritten digit recognition is described. The initial experimental results on a sample of handwritten digits from the United States Postal Service database and the National Institute of Standards and Technology database are very encouraging. Of the training data set of 3811 handwritten digits, 99.95% were recognized. The system was able to recognize 91.15% of the remaining 1909-digit never-before-seen test set. Upon training with all the 5720 digits, 99.34% were recognized
Keywords :
artificial intelligence; character recognition; feature extraction; neural nets; National Institute of Standards; United States Postal Service database; artificial neural network; character recognition; comprehensive features; handwritten digit recognition; septon feature scheme; thinned binary images; Artificial neural networks; Character recognition; Feature extraction; Handwriting recognition; Image databases; NIST; Postal services; Spatial databases; System testing; Training data;
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
DOI :
10.1109/IJCNN.1992.227107
