A modification of a stopping method for text recognition in a video stream with best frame selection

2021 ◽  
Author(s):  
Ilya Tolstov ◽  
Stanislav Martynov ◽  
Vera Farsobina ◽  
Konstantin Bulatov
Keyword(s):  
Video Stream ◽  
Text Recognition ◽  
Frame Selection

scholarly journals Weighted combination of per-frame recognition results for text recognition in a video stream

2021 ◽  
Vol 45 (1) ◽  
pp. 77-89
Author(s):  
O. Petrova ◽  
K. Bulatov ◽  
V.V. Arlazarov ◽  
V.L. Arlazarov
Keyword(s):  
Video Stream ◽  
Input Image ◽  
Document Image ◽  
Text Recognition ◽  
Weighting Method ◽  
Document Recognition ◽  
Perspective Distortion ◽  
Character Weighting ◽  
Specialized Equipment ◽  
Weighted Combination

The scope of uses of automated document recognition has extended and as a result, recognition techniques that do not require specialized equipment have become more relevant. Among such techniques, document recognition using mobile devices is of interest. However, it is not always possible to ensure controlled capturing conditions and, consequentially, high quality of input images. Unlike specialized scanners, mobile cameras allow using a video stream as an input, thus obtaining several images of the recognized object, captured with various characteristics. In this case, a problem of combining the information from multiple input frames arises. In this paper, we propose a weighing model for the process of combining the per-frame recognition results, two approaches to the weighted combination of the text recognition results, and two weighing criteria. The effectiveness of the proposed approaches is tested using datasets of identity documents captured with a mobile device camera in different conditions, including perspective distortion of the document image and low lighting conditions. The experimental results show that the weighting combination can improve the text recognition result quality in the video stream, and the per-character weighting method with input image focus estimation as a base criterion allows one to achieve the best results on the datasets analyzed.

Analysis of a stopping method for text recognition in video stream using an extended result model with per-character alternatives

2020 ◽  
Vol 34 (3) ◽  
pp. 217-225
Author(s):  
K. B. Bulatov ◽  
B. I. Savelyev ◽  
V. V. Arlazarov ◽  
N. V. Fedotova
Keyword(s):  
Video Stream ◽  
Text Recognition

Frame selection for OCR from video stream of book flipping

2017 ◽  
Vol 77 (1) ◽  
pp. 985-1008 ◽  
Author(s):  
Dibyayan Chakraborty ◽  
Partha Pratim Roy ◽  
Rajkumar Saini ◽  
Jose M. Alvarez ◽  
Umapada Pal
Keyword(s):  
Video Stream ◽  
Frame Selection ◽  
Selection For

Towards the Use of a Tree Branching Logic in the Environment of a Hospital Information System

1980 ◽  
Vol 19 (04) ◽  
pp. 187-194
Author(s):  
J.-Ph. Berney ◽  
R. Baud ◽  
J.-R. Scherrer
Keyword(s):  
Syntactic Structure ◽  
Large Data ◽  
Selection System ◽  
Frame Selection ◽  
Knowing That ◽  
Medical Language ◽  
Selection Systems ◽  
Definition Of ◽  
Man Machine Dialogue ◽  
Oriented System

It is well known that Frame Selection Systems (FFS) have proved both popular and effective in physician-machine and patient-machine dialogue. A formal algorithm for definition of a Frame Selection System for handling man-machine dialogue is presented here. Besides, it is shown how the natural medical language can be handled using the approach of a tree branching logic. This logic appears to be based upon ordered series of selections which enclose a syntactic structure. The external specifications are discussed with regard to convenience and efficiency. Knowing that all communication between the user and the application programmes is handled only by FSS software, FSS contributes to achieving modularity and, therefore, also maintainability in a transaction-oriented system with a large data base and concurrent accesses.

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

ALGORITHMS FOR IMAGE PRE-PROCESSING IN THE FACE IDENTIFICATION SYSTEM IN THE VIDEO STREAM

2019 ◽  
Vol 4 (91) ◽  
pp. 21-29 ◽  
Author(s):  
Yaroslav Trofimenko ◽  
Lyudmila Vinogradova ◽  
Evgeniy Ershov
Keyword(s):  
Video Stream ◽  
Face Identification ◽  
Identification System ◽  
The Face

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

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