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This paper proposes a writer identification and verification method using autoassociative neural networks. An autoassociative neural network is separately prepared for each category and doesn't depend on any other category in its learning processing, so it is easily applicable to the increase of categories to be distinguished. Weighted direction index histogram feature of 256 dimensions extracted from the handwritten character pat tern is used as an input value to this network. In the network, it is ideal when the output value will be equal to the input value. Therefore, writer identification is executed by compairing the errors between input and output values of every network, and writer verification is performed by compairing the error in the network with previously assigned threshold value. From the experiment applied to 20 persons, the correct identifi cation rate of 92.48% was obtained and the correct acceptance rate was 92.73% in the writer verification. Furthermore, it is clarified that the high precision writer recognition will be feasible by using the combined character patterns of the same category or the different category in the calculation of errors.

Writer Identification and Verification Using Autoassociative Neural Networks