Журнал «Труды Института системного анализа Российской академии наук» - A.E. Lynchenko, A.V.Sheshkus, V.L.Arlazarov Identity document classifiaction algorithm based on similarity metric robust to projective distortions

Просматривается номер 2018-S1

Data mining and image recognition

N.S. Skoryukina, A.N. Milovzorov, D.V. Polevoy, V.V. Arlazarov Paintings recognition in uncontrolled conditions using one-shot learning

A.E. Zhukovsky Methods for interframe integration of document detection results in a video stream of a mobile device

I.A. Kunina, E. I. Panfilova, M.A. Povolotskiy Zebra-crossing detection on road images using dynamic time warping

O.A. Slavin, V.L. Arlazarov Method for classifying recognized pages of administrative documents on the basis of text key points

O.O. Petrova, K.B. Bulatov Methods of machine-readable zone recognition results post-processing

A.E. Marchenko, E.I. Ershov, D.A. Shepelev, D.S. Sidorchuk, V.P. Bozhkova, D.P. Nikolaev Designing of language of description of observable properties of recognized objects in the absence of samples

Intellectual systems and technologies

E.E. Limonova, N.L. Rzhenev, A.V. Uskov, M.I. Neiman-zade Fast implementation of Hamming distance on VLIW-architectures on the example of Elbrus platform

V.V. Arlazarov, K.B. Bulatov, A.V. Uskov A model of object recognition system in video stream of a mobile device

A.A. Ivanova, S.A. Gladilin, A.E. Zhukovsky, E.L. Pliskin Database for the administrative accounting of scientific publications

A.S. Ingacheva, A.V. Sheshkus, T. S. Chernov, E.E. Limonova, V.V. Arlazarov X-ray computed tomography scanner – a new tool in recognition

N.O. Beshaposhnikov, A.G. Kushnirenko, A.A. Levin A method for auto-calibration of the educational robot control parameters using computer vision library OpenCV

Image and signal processing

A.E. Zhukovsky, E.E. Limonova, D.P. Nikolaev Exact implementation of common image processing algorithms using fully convolutional networks

V.E. Prun Reducing the influence of high-absorbing inclusions on CT reconstructions using algebraic reconstruction technique

B.I. Savelyev, I.B. Mamay, D.P. Nikolaev, V.L. Arlazarov, K.B. Bulatov, N.S. Skoryukina A method of projective transformations graph adjustment for panorama stitching problem for images of planar objects

D.V. Tropin, D.P. Nikolaev, D.G. Slugin The method of image alignment based on sharpness maximization

J.A. Shemiakina, A.E. Zhukovsky, I.A. Konovalenko, D.P. Nikolaev Algorithm for automatic framing of digital images under projective transformation

MACHINE LEARNING

A.V. Gayer, A.V. Sheshkus, Y.S. Chernyshova Augmentation on the fly for the neural networks learning

V.V. Arlazarov, D.P. Matalov, S.A. Usilin Localization of the seal on the identity document image using machine learning approach

A.E. Lynchenko, A.V.Sheshkus, V.L.Arlazarov Identity document classifiaction algorithm based on similarity metric robust to projective distortions

V.A. Malykh, V.A. Lyalin On Classification of Noisy Texts

Y.S. Chernyshova, M.A. Aliev, A.V. Sheshkus Optical font recognition of images captured with mobile devices and its application for detecting identity documents forgery

D.A. Ilin Fast words boundaries localization in text fields for low quality document images

D.E. Ivanov, D.V. Polevoy, D.L. Sholomov Selection of informative elements for the training of a lightweight convolutional neural network classifier in the conditions of a strong imbalance of the training sample


	A.E. Lynchenko, A.V.Sheshkus, V.L.Arlazarov Identity document classifiaction algorithm based on similarity metric robust to projective distortions
Abstract. This article presents an algorithm for document image recognition robust to projective distortions and with possibility of one-shot learning. This method is based on a similarity metric, which is learned using Siamese architecture. The proposed algorithm achieved recognition quality comparable to classifying convolutional network on an open dataset of identity document images MIDV-500. Keywords: pattern recognition, siamese neural network, convolutional neural network, deep learning. PP. 167-173. DOI: 10.14357/20790279180519 References 1. V.V. Arlazarov, K. Bulatov, T. Chernov and V.L. Arlazarov. “MIDV-500: A Dataset for Identity Documents”. 2. G. Hinton, L. Deng and D. Yu. “Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,” IEEE Signal Processing Magazine, pp. 82 – 97, DOI: 10.1109/MSP.2012.2205597, 18 10 2012. 3. R. Collobert and J. Weston. “A unified architecture for natural language processing: deep neural networks with multitask learning,” in Proceedings of the 25th international conference on Machine learning, Helsinki, Finland, 2008, DOI: 10.1145/1390156.1390177. 4. A. Krizhevsky, I. Sutskever and G. Hinton. “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems 25, pp. 1097-1105, 2012. 5. K. Bulatov, V.V. Arlazarov and T. Chernov. “Smart IDReader: Document Recognition in Video Stream,” International Conference on Document Analysis and Recognition (ICDAR), 9 11 2017, DOI: 10.1109/ICDAR.2017.347. 6. I. Dubnov, A. Merkov, V.L. Arlazarov and I. Nikolaev. “Evidence Maximization Technique for Training of Elastic Nets,” Journal of Optimization, 2016. 7. J. Goldberger, S. Roweis, G. Hinton and R. Salakhutdinov. “Neighbourhood Components Analysis,” in Neural Information Processing Systems (NIPS), Montreal, Canada, 2014. 8. S. Chopra, R. Hadsell and Y. LeCun. “Learning a Similarity Metric Discriminatively, with Application to Face Verification,” in Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005, DOI: 10.1109/CVPR.2005.202. 9. J. Bromley, I. Guyon, Y. LeCun and R. Shah. “Signature verification using a Siamese time delay neural network,” in Advances in neural information processing systems (NIPS), 1993. 10. O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu and D. Wierstra. “Matching Networks for One Shot Learning,” Advances in Neural Information Processing Systems 29, pp. 3630-3638, 2016. 11. D. Polevoy, A.Sheshkus, K.Bulatov, N. Skoryukina, T. Chernov and V. V. Arlazarov. “Key Aspects of Document Recognition Using Small Digital Cameras,” Vestnik RFBR, pp. 97-108, 2016, DOI: 10.22204/2410-4639-2016-092-04-97-108. 12. S. Lawrence, L. Giles and A. Back. “Face recognition: a convolutional neural-network approach,” IEEE Transactions on Neural Networks, pp. 98 – 113, 1 1 1997, DOI: 10.1109/72.554195.

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