Title :
Handwritten numeral recognition using MFNN based multiexpert combination strategy
Author :
Xiaofan Lin ; Ding, Xiaoqing ; Wu, Youshou
Author_Institution :
Dept. of Electron. Eng., Tsinghua Univ., Beijing, China
Abstract :
A novel unconstrained numeral recognition system using an MFNN (multilayered feedforward neural net)-based multi-expert combination strategy is proposed. Taking advantage of every method´s confidence value as well as its output label, this strategy can improve the system´s performance significantly with as few as two experts. Another novel feature of the system is that the multi-expert combination problem is converted into a new classification problem, and then the MFNNs are used to handle the combination adaptively. The proposed system has achieved promising results on the NIST database
Keywords :
adaptive systems; feedforward neural nets; handwriting recognition; image classification; optical character recognition; performance evaluation; NIST database; classification problem; classifier combination; confidence value; handwritten numeral recognition; multi-expert combination strategy; multilayered feedforward neural nets; output label; system performance; unconstrained numeral recognition system; Feedforward neural networks; Feedforward systems; Handwriting recognition; Humans; Image processing; Image recognition; Multi-layer neural network; Neural networks; Pattern recognition; Spatial databases;
Conference_Titel :
Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on
Conference_Location :
Ulm
Print_ISBN :
0-8186-7898-4
DOI :
10.1109/ICDAR.1997.620542
