Vladimir V. Arlazarov

Method of determining the necessary number of observations for video stream documents recognition

This paper discusses a task of document recognition on a sequence of video frames. In order to optimize the processing speed an estimation is performed of stability of recognition results obtained from several video frames. Considering identity document (Russian internal passport) recognition on a mobile device it is shown that significant decrease is possible of the number of observations necessary for obtaining precise recognition result.

Performance improvement of multi-class detection using greedy algorithm for Viola-Jones cascade selection

This paper aims to study the problem of multi-class object detection in video stream with Viola-Jones cascades. An adaptive algorithm for selecting Viola-Jones cascade based on greedy choice strategy in solution of the N-armed bandit problem is proposed. The efficiency of the algorithm on the problem of detection and recognition of the bank card logos in the video stream is shown. The proposed algorithm can be effectively used in documents localization and identification, recognition of road scene elements, localization and tracking of the lengthy objects , and for solving other problems of rigid object detection in a heterogeneous data flows. The computational efficiency of the algorithm makes it possible to use it both on personal computers and on mobile devices based on processors with low power consumption.

Textual blocks rectification method based on fast Hough transform analysis in identity documents recognition

Textual blocks rectification or slant correction is an important stage of document image processing in OCR systems. This paper considers existing methods and introduces an approach for the construction of such algorithms based on Fast Hough Transform analysis. A quality measurement technique is proposed and obtained results are shown for both printed and handwritten textual blocks processing as a part of an industrial system of identity documents recognition on mobile devices.

Image quality assessment for video stream recognition systems

Recognition and machine vision systems have long been widely used in many disciplines to automate various processes of life and industry. Input images of optical recognition systems can be subjected to a large number of different distortions, especially in uncontrolled or natural shooting conditions, which leads to unpredictable results of recognition systems, making it impossible to assess their reliability. For this reason, it is necessary to perform quality control of the input data of recognition systems, which is facilitated by modern progress in the field of image quality evaluation. In this paper, we investigate the approach to designing optical recognition systems with built-in input image quality estimation modules and feedback, for which the necessary definitions are introduced and a model for describing such systems is constructed. The efficiency of this approach is illustrated by the example of solving the problem of selecting the best frames for recognition in a video stream for a system with limited resources. Experimental results are presented for the system for identity documents recognition, showing a significant increase in the accuracy and speed of the system under simulated conditions of automatic camera focusing, leading to blurring of frames.

Comparison of the scanned pages of the contractual documents

In this paper the problem statement is given to compare the digitized pages of the official papers. Such problem appears during the comparison of two customer copies signed at different times between two parties with a view to find the possible modifications introduced on the one hand. This problem is a practically significant in the banking sector during the conclusion of contracts in a paper format. The method of comparison based on the recognition, which consists in the comparison of two bag-of-words, which are the recognition result of the master and test pages, is suggested. The described experiments were conducted using the OCR Tesseract and the siamese neural network. The advantages of the suggested method are the steady operation of the comparison algorithm and the high exacting precision, and one of the disadvantages is the dependence on the chosen OCR.

Fast integer approximations in convolutional neural networks using layer-by-layer training

This paper explores method of layer-by-layer training for neural networks to train neural network, that use approximate calculations and/or low precision data types. Proposed method allows to improve recognition accuracy using standard training algorithms and tools. At the same time, it allows to speed up neural network calculations using fast-processed approximate calculations and compact data types. We consider 8-bit fixed-point arithmetic as the example of such approximation for image recognition problems. In the end, we show significant accuracy increase for considered approximation along with processing speedup.

Snapscreen: TV-stream frame search with projectively distorted and noisy query

In this work we describe an approach to real-time image search in large databases robust to variety of query distortions such as lighting alterations, projective distortions or digital noise. The approach is based on the extraction of keypoints and their descriptors, random hierarchical clustering trees for preliminary search and RANSAC for refining search and result scoring. The algorithm is implemented in Snapscreen system which allows determining a TV-channel and a TV-show from a picture acquired with mobile device. The implementation is enhanced using preceding localization of screen region. Results for the real-world data with different modifications of the system are presented.

Slant rectification in Russian passport OCR system using fast Hough transform

In this paper, we introduce slant detection method based on Fast Hough Transform calculation and demonstrate its application in industrial system for Russian passports recognition. About 1.5% of this kind of documents appear to be slant or italic. This fact reduces recognition rate, because Optical Recognition Systems are normally designed to process normal fonts. Our method uses Fast Hough Transform to analyse vertical strokes of characters extracted with the help of x-derivative of a text line image. To improve the quality of detector we also introduce field grouping rules. The resulting algorithm allowed to reach high detection quality. Almost all errors of considered approach happen on passports of nonstandard fonts, while slant detector works in appropriate way.

Segments graph-based approach for smartphone document capture

Document capture with a smartphone camera is already here to stay. Interactive applications for document capture and its enhancement have filled mobile application stores. However, discounting the predictions and judging only from the experience of using such applications, they are not yet ready to compete with stationary scanners when high quality and reliability is required. This paper is devoted to analysis of the problem of document detection in the image and evaluation of the quality of existing mobile applications. Based on this analysis we present a new reliable algorithm for document capture, based on the boundary segments detection and constructing a segments graph to fit rectangular projective model. The algorithm achieves about 95% quality of document detection and outperforms all of the reviewed algorithms, implemented in mobile applications.