MIDV-2020: a comprehensive benchmark dataset for identity document analysis

Identity documents recognition is an important sub-field of document analysis, which deals with tasks of robust document detection, type identification, text fields recognition, as well as identity fraud prevention and document authenticity validation given photos, scans, or video frames of an identity document capture. Significant amount of research has been published on this topic in recent years, however a chief difficulty for such research is scarcity of datasets, due to the subject matter being protected by security requirements. A few datasets of identity documents which are available lack diversity of document types, capturing conditions, or variability of document field values. In this paper, we present a dataset MIDV-2020 which consists of 1000 video clips, 2000 scanned images, and 1000 photos of 1000 unique mock identity documents, each with unique text field values and unique artificially generated faces, with rich annotation. The dataset contains 72409 annotated images in total, making it the largest publicly available identity document dataset to the date of publication. We describe the structure of the dataset, its content and annotations, and present baseline experimental results to serve as a basis for future research. For the task of document location and identification content-independent, feature-based, and semantic segmentation-based methods were evaluated. For the task of document text field recognition, the Tesseract system was evaluated on field and character levels with grouping by field alphabets and document types. For the task of face detection, the performance of Multi Task Cascaded Convolutional Neural Networks-based method was evaluated separately for different types of image input modes. The baseline evaluations show that the existing methods of identity document analysis have a lot of room for improvement given modern challenges. We believe that the proposed dataset will prove invaluable for advancement of the field of document analysis and recognition.This work is partially supported by Russian Foundation for Basic Research (projects 19-29-09066 and 19-29-09092). All source images for MIDV-2020 dataset were obtained from Wikimedia Commons. Author attributions for each source images are listed in the original MIDV-500 description table (ftp://smartengines.com/midv-500/documents.pdf). Face images by Generated Photos (https://generated.photos)

Stability of nonlinear repetitive processes with possible failures

Nonlinear discrete-time repetitive processes with Markovian jumps are considered. For such processes stability analysis is developed and this result is then applied to iterative learning control design.Stability of nonlinear repetitive processes has not been developed previously in the current literature. This paper proposes and characterizes a stability theory for nonlinear repetitive processes that includes stability along the pass of linear examples as a special case.For considered systems the second Lyapunov method cannot be used. Because repetitive processes belong to a class of 2D systems in which state variables are depend on two independent variables and cannot be solved using all first differences of state variables. It is not allow us to find a first difference of Lyapunov function along the trajectory of the system without finding solution of a system of equations that fully excludes a main advantage of second Lyapunov method. At the same time the use of vector Lyapunov functions and discrete-time counterpart of the divergence operator of this function along the trajectories of system instead of first difference allow us to obtain constructive results.In this paper based on vector Lyapunov function approach sufficient conditions for pass profile exponential stability are obtained which in the linear case are obtained in terms of linear matrix inequalities and in the linear case without failures these conditions are reduced to known conditions of stability along the passA major application area where repetitive process stability theory can be used is Iterative Learning Control (ILC). The idea of ILC is following.If the system repeats the same finite duration operation over and over again, it is reasonable to use the input and output variables on the current pass for improving accuracy of performance of operations on the next pass.The new theoretical stability results are applied to ILC design under possible information failures. The ILC law convergence reduces to pass profile stability analysis. Computation and modeling of the system have been carried out using a simplified model of a vertical axis dynamics of a gantry robot.</p

Dissipativity and stabilization of nonlinear repetitive processes

Repetitive processes are characterized by repeated executions of a task defined over a finite duration with resetting after each execution is complete. Also the output from any execution directly influences the output produced on the next execution. The repetitive process model structure arises in the modeling of physical processes and can also be used to effect in the control of other systems, the design of iterative learning control laws where experimental verification of designs has been reported. The existing systems theory for them is, in the main, linear model based. This paper considers nonlinear repetitive processes using a dissipative setting and develops a stabilizing control law with the required conditions expressed in terms of vector storage functions. This design is then extended to stabilization plus disturbance attenuation

Stability of nonlinear discrete repetitive processes with Markovian switching

Repetitive processes are a class of 2D systems that operate over a subset of the upper-right quadrant ofthe 2D plane. Applications include iterative learning control where experimental verification has beenreported based on a linear time-invariant model approximation of the dynamics. This paper considersdiscrete nonlinear repetitive processes with Markovian switching and applies, as one application, theresulting stability theory to iterative learning control for a class of networked systems where time-varyingdynamics arise.<br/

2D Excitons as Primary Energy Carriers in Organic Crystals: The Case of Oligoacenes

A number of organic crystals show anisotropic excitonic couplings, with weak interlayer interactions between molecules that are more strongly coupled within the layers. The resulting energy carriers are intralayer 2D excitons that diffuse along the interlayer direction. We model this analytically for infinite layers and using quantum-chemical calculations of the electronic couplings for anthracene clusters. We show that the exciton hopping rates and diffusion lengths depend in a subtle manner on the size and shape of the interacting aggregates, temperature, and the presence of energetic disorder.European Union. MODECOM (NMP3-CT-2006-016434)European Union. ONE-P (NMP3-LA-2008-212311)Fonds national de la recherche scientifique (Belgium)The Inter University Computation Center (Israel)United States. Dept. of Energy. Office of ScienceUnited States. Dept. of Energy. Office of Basic EnergyUnited States. Dept. of Energy (Grant No DE-SC0001088