Automatic Chinese Handwriting Verification Algorithm Using Deep Neural Networks Handwriting verification is to identify whether a script was written by a person himself or forged. Conventional handwriting verification algorithms are based on feature extraction. However, the features of scripts are highly affected by the writing instrument, the posture, and the force of writing, even if the scripts were written by the same person, the extracted features will be quite different. Moreover, since some writers might not write some strokes clearly or ignore some strokes, not all features can be well extracted in every script. Therefore, in this paper, we apply a deep neural network based algorithm for handwriting verification. With the proposed algorithm, the parts that are really powerful and robust for handwriting verification can be highlighted by the autoencoder. Then, a very high accurate handwriting verification result can be achieved.

Characters Verification via Siamese Convolutional Neural Network In the printing and carving industries, it is necessary to check whether printed outputs or carved wares are missing or etched through comparing the drawings. Traditional approaches and identification methods canxe2x80x99t be used for this application where the number of character categories are not determined, and where the character may be unique designed by manufacturer. Driven by the onetoone matching pattern, we propose an endtoend dual input network for automatic comparison, which uses convolutional neural network to extract features from the scanned images which collected from printed matters. Then, we convert the corresponding drawing to the vector of the same dimension to calculate distance and the matchmismatch result. Experiments show that our method can effectively solve the problem of character comparison with many types, and at the same time propose an automated comparing program for the industrial imprinting process.