In many of these applications recognition errors are extremely costly.
#Video text recognition software registration
These technologies have found application when filling out various registration forms, identifying a person in security systems, filling out personal and sensitive information, etc.
A separate important subsection of document recognition is the identity documents recognition. The translation of paper documents into electronic form allows quickly and conveniently to process and index them. Īn important area of OCR technologies application is document recognition. Besides, more and more cases require the possibility to use "improvised means" for the recognition, with input images captured using a smartphone camera or a web-camera. Along with the applicability of the text recognition technologies, the requirements for the quality and reliability of recognition results are increasing. Tance systems, assistance for people with visual impairments, online translators, government photo and video recording systems, and many more.
#Video text recognition software driver
The development of hardware, such as personal mobile devices, has made it possible to expand the applicability of OCR technologies for recognizing text in natural scenes and use these technologies in such cases as driver assis. Apart from the automatic input of personal data, text object recognition is essential in electronic document management systems, allows saving time, reducing expenses, and saving natural resources. For instance, automatic personal data entry can be done without the use of specialized equipment, for example, when opening a bank account using a mobile application or when buying and registering SIM cards in a selfservice mode. But today the scope of application of such technologies has expanded, and document recognition is increasingly carried out in uncontrolled capturing conditions. A few decades ago such problems required special equipment, knowledge of the used fonts, scanned image characteristics, etc. One of the first problems in which optical character recognition (OCR) technologies found their application was automatic data entry. Nowadays text object recognition is widely used not only in government and business processes but also in everyday life. DOI: 10.18287/2412-6179-CO-795.Īcknowledgements: This work is partially supported by the Russian Foundation for Basic Research (projects 17-29-03236 and 18-07-01387).ĭocument recognition in uncontrolled conditions Weighted combination of perframe recognition results for text recognition in a video stream.
Keywords: mobile OCR, video stream, anytime algorithms, weighted combination, ensemble methods.Ĭitation: Petrova O, Bulatov K, Arlazarov VV, Arlazarov VL. Arlazarov1,2,3 1FRC CSC RAS, Moscow, Russia, 2 Smart Engines Service LLC, Moscow, Russia, 3 Moscow Institute of Physics and Technology (State University), Moscow, Russia Weighted combination of per-frame recognition results for text recognition in a video stream 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.
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. 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. In this case, a problem of combining the information from multiple input frames arises. 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.
However, it is not always possible to ensure controlled capturing conditions and, consequentially, high quality of input images. Among such techniques, document recognition using mobile devices is of interest. 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.
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