Document image recognition algorithm based on similarity metric robust to projective distortions for mobile devices

Aleksander Lynchenko; Aleksander Sheshkus; Vladimir L. Arlazarov

doi:10.1117/12.2523152

15 March 2019 Document image recognition algorithm based on similarity metric robust to projective distortions for mobile devices

Aleksander Lynchenko, Aleksander Sheshkus , Vladimir L. Arlazarov

Author Affiliations +

Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 110411K (2019) https://doi.org/10.1117/12.2523152
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany

Abstract

The paper presents an algorithm for document image recognition robust to projective distortions. This algorithm is based on a similarity metric, which is learned using Siamese architecture. The idea of training Siamese networks is to build a function of converting the image into a space where a distance function corresponding to a pre-defined metric approximates the similarity between objects of initial space. During learning the loss function tries to minimize the distance between pairs of object from the same class and maximize it between the ones from different classes. A convolutional network is used for mapping initial space to the target one. This network lets to construct a feature vector in target space for each class. Classification of objects is performed using the mapping function and finding the nearest feature vector. The proposed algorithm achieved recognition quality comparable to classifying convolutional network on an open dataset of document images MIDV-500 [1]. Another important advantage of this method is the possibility of one-shot learning that is also shown in the paper.

Citation Download Citation

Aleksander Lynchenko, Aleksander Sheshkus , and Vladimir L. Arlazarov "Document image recognition algorithm based on similarity metric robust to projective distortions for mobile devices", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110411K (15 March 2019); https://doi.org/10.1117/12.2523152

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