A Business Card Reader Application for iOS devices based on Tesseract

Dangiwa, Bello Ahmed; Kumar, Smitha S

doi:10.1109/cspis.2018.8642727

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…Let B jk denote the sum of elements with such indices: B jk = i∈L jk y ijk . By performing separate summations across indices in L jk we can remove the absolute value bars in the expression (7):…”

Section: B Unweighted Casementioning

confidence: 99%

“…The complexity of computing the estimation on stage n is O(nS n K(S n + M )). 2) Method A. Estimation is computed using an approximate modelling using direct summation (6) with ∆ ijk computed using a simplified expression (7) for unweighted case. The complexity of computing the estimation on stage n is O(nS n K).…”

Section: A Experimental Settingmentioning

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%

See 2 more Smart Citations

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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Section: B Unweighted Casementioning

confidence: 99%

Section: A Experimental Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Literatürde, İngilizce [5], Vietnamca [6], Japonca [7], Çince [8,9] dillerinde hazırlanmış kâğıt tabanlı kartvizitlerde yer alan bilgilerinin dijitalleştirilmesi ve çıkarılması amacıyla Mobile Vision, OpenCV ve Tesseract [5][6][7][8][9][10][11] gibi çeşitli görüntü işleme kütüphaneleri kullanarak geliştirilmiş uygulamalar yer almaktadır. Bu kapsamda, Thuan ve arkadaşları Google Vision kütüphanesini kullanarak İngilizce hazırlanmış kâğıt tabanlı kartvizitler üzerinde yer alan bilgilerin OCR yöntemi ile dijitalleştirilmesini gerçekleştirmiştir [5].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Cloud Based WEB Application Design for Automatic Turkish Business Card Recognition and Its Performance Evaluation

ŞAHİN

Uçar

Solak

2022

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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In this study, a cloud-based web application is developed that extracts information such as names, phone numbers, e-mail addresses, job titles from physical business cards prepared in Turkish using Tesseract-based optical character recognition method (OCR). Figure A. System architecture of the proposed cloud-based business card reader application Purpose: In the literature, there are various applications developed based on Mobile Vision, OpenCV and Tesseract for the digitization and extraction of information on paper-based business cards in English, Vietnamese, Japanese and Chinese languages. In this study, it is aimed to develop a high-accuracy cloud-based business card recognition software (TRCardScan) compatible with Turkish language with Tesseract-based OCR in order to extract the information from paper-based business cards in Turkish.Theory and Methods: The system architecture of the proposed paper-based Turkish business card reader application is given in Figure A. As can be seen, firstly, business card photos, are taken from the camera or image gallery as input to the application. Then, these photos are subjected to character reading process with Tesseract-based OCR method. As a result of the OCR process, the read and converted texts such as name, surname, mobile phone, e-mail address and contact address on the business card are parsed by algorithms specific to the characteristic of the parts in which it is located. In the last stage, the data that is parsed and transformed into meaningful information is sent to the web service to be written in the relevant field in the database. Results:In the analyzes made with 15 paper-based Turkish business cards with different features, it was observed that the proposed TRCardScan software was able to extract the information from physical business cards with 84.76% accuracy, 96.05% precision, 84.88% recall and 90.12% F1 score. In addition, the average extraction time per business card is 1.6 seconds. Conclusion:The proposed TRCardScan can read and parse data from physical business cards with an average extraction time of 1.6 seconds and high accuracy of around 85%. These results show that the parsing algorithm designed for the Turkish language in the proposed web-based application is successful, considering the time and performance criteria. Finally, when compared to similar software, TRCardScan is considered to be quite successful with its high accuracy rates and reasonable extraction times.

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“…Modern document entry systems allow to automatize the process of data extraction from various documents, either business, regulatory, or personal. Such systems are used for creating digital archives of historical documents [1], recognition of small-scale documents such as business cards [2], ID documents, driving licences, passports [3], as well as large-scale business documents [4].…”

Section: Introductionmentioning

confidence: 99%

Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternatives

Bulatov

Savelyev

Arlazarov

2020

Twelfth International Conference on Machine Vision (ICMV 2019)

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In the field of document analysis and recognition using mobile devices for capturing, and the field of object recognition in a video stream, an important problem is determining the time when the capturing process should be stopped. Efficient stopping influences not only the total time spent for performing recognition and data entry, but the expected accuracy of the result as well. This paper is directed on extending the stopping method based on next integrated recognition result modelling, in order for it to be used within a string result recognition model with per-character alternatives. The stopping method and notes on its extension are described, and experimental evaluation is performed on an open dataset MIDV-500. The method was compares with previously published methods based on input observations clustering. The obtained results indicate that the stopping method based on the next integrated result modelling allows to achieve higher accuracy, even when compared with the best achievable configuration of the competing methods.

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A Business Card Reader Application for iOS devices based on Tesseract

Cited by 10 publications

References 8 publications

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

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

Cloud Based WEB Application Design for Automatic Turkish Business Card Recognition and Its Performance Evaluation

Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternatives

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