A new method for text detection and recognition in indoor scene for assisting blind people

Jabnoun, Hanen; Benzarti, Faouzi; Amiri, Hamid

doi:10.1117/12.2268399

Cited by 10 publications

(5 citation statements)

References 3 publications

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“…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 [4]. 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-tance systems [5], assistance for people with visual impairments [6], online translators [7], government photo and video recording systems [8,9], and many more. Along with the applicability of the text recognition technologies, the requirements for the quality and reliability of recognition results are increasing [10].…”

Section: Document Recognition In Uncontrolled Conditionsmentioning

confidence: 99%

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

Petrova¹,

Bulatov

Arlazarov³

2021

Computer Optics

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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.

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Section: Document Recognition In Uncontrolled Conditionsmentioning

confidence: 99%

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

Petrova¹,

Bulatov

Arlazarov³

2021

Computer Optics

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“…, X n , X i )) is not higher than S n , the approximation 3 in its essence relies on an assumption of local linearity of function f (t) = (2t)/(t + 2) in the range t ∈ [0.0, 1.0]. Using the optimization ( 8) and balanced binary search trees we can efficiently compute the approximation of G n , and then use the approximate equation (10) to convert it to a sum of normalized GLD between the current result and the modelled candidates for the next one, required to compute the estimation ∆n .…”

Section: Using a Normalized Gldmentioning

confidence: 99%

“…This signifies that the approximations 1 and 2 (naive alignment and naive Levenshtein) introduced in subsection III-A are justified for efficient approximate computation of the expected distance estimation. The approximation error of Method B is an effect of the naive normalization (10).…”

Section: B Comparing the Distance Estimation Valuementioning

confidence: 99%

“…One of the most relevant computer vision problems is automated data entry by means of text recognition. Text detection and recognition have applications in such fields as business processes automation, road traffic monitoring, government services, mobile payments [7]- [9], life augmentation for people with disabilities [10] and more. The research is targeted on improving the speed and accuracy of camerabased information extraction, given particular challenging conditions, such as poor illumination, low camera quality [11], optical distortions and noise, poor focus and motion blur [12].…”

Section: Introductionmentioning

confidence: 99%

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Fast Approximate Modelling of the Next Combination Result for Stopping the Text Recognition in a Video

Bulatov¹,

Fedotova²,

Arlazarov³

2020

Preprint

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“…Optical character recognition systems become widely used in a variety of fields as a part of business, social, and government process automation [1][2][3][4]. Such systems can take scanned images, photos, and video sequences as an input.…”

Section: Introductionmentioning

confidence: 99%

Methods of weighted combination for text field recognition in a video stream

Petrova

Bulatov

Arlazarov

2020

Twelfth International Conference on Machine Vision (ICMV 2019)

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Due to a noticeable expansion of document recognition applicability, there is a high demand for recognition on mobile devices. A mobile camera, unlike a scanner, cannot always ensure the absence of various image distortions, therefore the task of improving the recognition precision is relevant. The advantage of mobile devices over scanners is the ability to use video stream input, which allows to get multiple images of a recognized document. Despite this, not enough attention is currently paid to the issue of combining recognition results obtained from different frames when using video stream input. In this paper we propose a weighted text string recognition results combination method and weighting criteria, and provide experimental data for verifying their validity and effectiveness. Based on the obtained results, it is concluded that the use of such weighted combination is appropriate for improving the quality of the video stream recognition result.

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A new method for text detection and recognition in indoor scene for assisting blind people

Cited by 10 publications

References 3 publications

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

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

Fast Approximate Modelling of the Next Combination Result for Stopping the Text Recognition in a Video

Methods of weighted combination for text field recognition in a video stream

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