The paper proposes an approach for matching of digitized copies of business documents. This task arises when comparing two versions of the same document, genuine and forgery, to find possible modifications, for example in the banking sector during the conclusion of contracts in paper form to avoid possible fraud. The matching method of two documents based on comparison images of text lines using Variational Autoencoder (VAE) trained on genuine images and calculation Fisher information metric to find modifications. Experiments were conducted on the public Payslips dataset (in French). The results show the high quality and reliability of finding document forgeries and are compared to the results of the method which applies OCR and image matching.
The paper proposes an approach to training a convolutional neural network using information on the level of distortion of input data. The learning process is modified with an additional layer, which is subsequently deleted, so the architecture of the original network does not change. As an example, the LeNet5 architecture network with training data based on the MNIST symbols and a distortion model as Gaussian blur with a variable level of distortion is considered. This approach does not have quality loss of the network and has a significant error-free zone in responses on the test data which is absent in the traditional approach to training. The responses are statistically dependent on the level of input image’s distortions and there is a presence of a strong relationship between them.
In this paper the method of image alignment based on average image sharpness maximization is proposed. The algorithm for global-shift model is investigated, its efficiency by applying FFT is shown. For projective model, an approach for image alignment using local shifts and RANSAC to obtain the final transform is considered. Experimental results for the system of document's reconstruction in a video stream increasing quality of output image are demonstrated.
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