The classification of handwritten digits through an analog feature extractor chip and neural classifier is discussed in this paper. The chip implements a feature extraction algorithm onto analog circuits; it extracts a set of 112 features from the input character (32 x 24 binary pixel matrix). The features, coded by current signals, are given in input to a neural classifier which performs the recognition task. The chip validation results are reported: a set of handwritten digits have been classified by a neural network implementation by a software simulator. The resulting classification error rate has been successfully compared with the ones obtained by high level mo0del of the chip and to those obtained with other techniques reported in the literature.
Recognizing Handwritten Digits with a Dedicated Analog VLSI Feature Extractor
CAVIGLIA, DANIELE;VALLE, MAURIZIO
1998
Abstract
The classification of handwritten digits through an analog feature extractor chip and neural classifier is discussed in this paper. The chip implements a feature extraction algorithm onto analog circuits; it extracts a set of 112 features from the input character (32 x 24 binary pixel matrix). The features, coded by current signals, are given in input to a neural classifier which performs the recognition task. The chip validation results are reported: a set of handwritten digits have been classified by a neural network implementation by a software simulator. The resulting classification error rate has been successfully compared with the ones obtained by high level mo0del of the chip and to those obtained with other techniques reported in the literature.
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