Title :
Improved neocognitron with bend-detecting cells
Author :
Fukushima, Kunihiko ; Wake, Nobuaki
Author_Institution :
Fac. of Eng. Sci., Osaka Univ., Japan
Abstract :
The authors present an improved neocognitron, in which bend-detecting cells, as well as line-extracting cells, are utilized. In contrast to the conventional neocognitron, which has shown a lesser ability to recognize deformed patterns when trained using unsupervised learning than by using supervised learning, the new system shows considerable robustness even when trained by unsupervised learning. The new system can also accept some variation in the line thickness of the input patterns. Edge-extracting cells, which are built into the network, are effectively used for this purpose. A line is extracted from the edges on both sides of the line. To demonstrate the performance of the new system, the network has been trained to recognize handwritten numerals
Keywords :
character recognition; neural nets; unsupervised learning; C-cells; S-cells; bend-detecting cells; edge extracting cells; handwritten numerals; hierarchical neural net structure; line thickness; line-extracting cells; neocognitron; unsupervised learning; Biological neural networks; Brain modeling; Character recognition; Feature extraction; Handwriting recognition; Pattern recognition; Robustness; Supervised learning; Unsupervised learning; Virtual colonoscopy;
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
DOI :
10.1109/IJCNN.1992.227343