The demand for on-device document recognition systems increases in conjunction with the emergence of more strict privacy and security requirements. In such systems, there is no data transfer from the end device to a third-party information processing servers. The response time is vital to the user experience of on-device document recognition. Combined with the unavailability of discrete GPUs, powerful CPUs, or a large RAM capacity on consumer-grade end devices such as smartphones, the time limitations put significant constraints on the computational complexity of the applied algorithms for on-device execution.
In this work, we consider document location in an image without prior knowledge of the docu-ment content or its internal structure. In accordance with the published works, at least 5 systems offer solutions for on-device document location. All these systems use a location method which can be considered Hough-based. The precision of such systems seems to be lower than that of the state-of-the-art solutions which were not designed to account for the limited computational resources.
We propose an advanced Hough-based method. In contrast with other approaches, it accounts for the geometric invariants of the central projection model and combines both edge and color features for document boundary detection. The proposed method allowed for the second best result for SmartDoc dataset in terms of precision, surpassed by U-net like neural network. When evaluated on a more challenging MIDV-500 dataset, the proposed algorithm guaranteed the best precision compared to published methods. Our method retained the applicability to on-device computations.
Nowadays computed tomography provides the possibility to image internal cellular structures of embryos. A major challenge is a high accuracy image segmentation of tissues and individual cells. The process of manual image segmentation is time consuming and error prone. It can be partially replaced or augmented by cell modelling techniques developed by computer scientists based on biological, physiological and statistical properties of real embryos.
Four post-socialist countries of Eastern Europe (Poland, Hungary, Romania and Russia) were comparatively analyzed in terms of the relative position and dynamics of different types of territories over a ten-year period from 2010 to 2020. The author develops methodological ideas about the usage of individual typology to create a socio-economic profile of a territory in order to identify spatial inequality of the countries under study upon socio-demographic, economic and infrastructural indicators. The results of the analysis allowed concluding about dominant trends of socio-economic differentiation between rural, urban and periurban zones. It was found that the intraregional spatial convergence is not an obligatory consequence of the socio-economic development of a country. The transformation processes which accompany the post-socialist transition led to the formation of different models of spatial socio-economic differentiation in each country, i. e. relatively balanced development in Poland, zonal development in Hungary and differentiated development in Romania and European Russia. The author suggests a concept of main stages and directions of the socio-economic differentiation of territories, which helps to systematize statistically-obtained conclusions.
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