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Adaptive Processing Parameter Adjustment by Feedback Recognition Method with Inverse Recall Neural Network Model
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1994/07/25
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Document Analysis and Recognition)
pattern recognition, neurocomputing, artificial intelligence,
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A feedback pattern recognition method using an inverse recall neural network model is proposed. The feedback method can adjust processing parameter values adaptively to individual patterns so as to produce reliable recognition results. In order to apply an adaptive control technique to such pattern recognition processings, the evaluation value for recognition uncertainty is determined to be a function with regard to an input pattern and processing parameters. In its feedback phase, the input pattern is fixed and processing parameters are adjusted to decrease the recognition uncertainty. The proposed neural network model implements two functions in this feedback recognition method. One is a discrimination as a kind of multi-layer feedforward model. The other is to generate an input modification so as to decrease the recognition uncertainty. The modification values indicate parts which are important for more certain recognition but are missed in the original input to the nerwork. The proposed feedback method can adjust prcessing parameter values in order to detect the important parts shown by the inverse recall network model. As explained in this paper, feature extraction parameter values are adaptively adjusted by this feedback method. After the inverse recall model and the feedback function are implemented, features are extracted again by using the modified feature extraction parameter values. The feature is classified by the feedforward function of the network model. The feedforward and feedback processings are repeated until a certain recognition result is obtained. This method was examined for hadwritten alpha-numerics with rotation distortion. The feedback method was found to decrease the rejection ratio at the same substitution error ratio with high efficiency.