Document localization algorithms based on feature points and straight lines

Skoryukina, Natalya; Shemiakina, Julia; Arlazarov, Vladimir V.; Faradjev, Igor

doi:10.1117/12.2311478

Cited by 17 publications

(13 citation statements)

References 3 publications

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“…For the optimization function to be constructed we need to define the model of a noisy segments set on the image plane. Let us denote the sought vanishing point by 2  v . Let us consider the set of n ideal line segments with endpoints referred to as…”

Section: Probabilistic Model Of a Noisy Segments Set Converging To A mentioning

confidence: 99%

“…If an object template is known and contains many static elements the object can be located and rectified simultaneously with local features [1,2]. In case of a rectangular object with a poor or unknown template we can detect its borders [3,4,5] and then calculate homogrpahy from the found quadrangle to a rectangular with known aspect ratio.…”

Section: Introductionmentioning

confidence: 99%

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Fast projective image rectification for planar objects with Manhattan structure

Shemiakina

Konovalenko

Tropin

et al. 2020

Twelfth International Conference on Machine Vision (ICMV 2019)

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This paper presents a method for metric rectification of planar objects that preserves angles and length ratios. An inner structure of an object is assumed to follow the laws of Manhattan World i.e. the majority of line segments are aligned with two orthogonal directions of the object. For that purpose we introduce the method that estimates the position of two vanishing points corresponding to the main object directions. It is based on an original optimization function of segments that estimates a vanishing point position. For calculation of the rectification homography with two vanishing points we propose a new method based on estimation of the camera rotation so that the camera axis is perpendicular to the object plane.The proposed method can be applied for rectification of various objects such as documents or building facades. Also since the camera rotation is estimated the method can be employed for estimation of object orientation (for example, during a surgery with radiograph of osteosynthesis implants). The method was evaluated on the MIDV-500 dataset containing projectively distorted images of documents with complex background. According to the experimental results an accuracy of the proposed method is better or equal to the-state-of-the-art if the background occupies no more than half of the image. Runtime of the method is around 3ms on core i7 3610qm CPU.

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Section: Probabilistic Model Of a Noisy Segments Set Converging To A mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast projective image rectification for planar objects with Manhattan structure

Shemiakina

Konovalenko

Tropin

et al. 2020

Twelfth International Conference on Machine Vision (ICMV 2019)

Self Cite

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“…Localization quality is one of the main requirements for the localization process of an LMS. Reliable localization should be based on unambiguous and understandable language, the appropriate language level, standardization of terminology, provision of sufficient context to the translators, and validation of the target text [19,22,25].…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of the Localization Quality of the Arabic Versions of Learning Management Systems

Omar¹

2021

IJACSA

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The recent years have witnessed the development of numerous Learning Management Systems (LMSs) to address the increasing needs of individuals and institutions all over the world. For accessibility and commercial purposes, many of these LMSs are released in different languages using what is known as localization systems. In this regard, there has been a parallel between the development of LMSs on one hand and the localization systems on the other. One main aspect in the recent evaluation systems and studies of LMSs is localization quality. Despite the prolific literature on localization quality, very little has been done on Arabic localization. As thus, this study is concerned with the evaluation of the localization quality of the Arabic version of LMSs. In order to explore users' perceptions towards the Arabic versions of the LMSs, an online questionnaire was conducted. Participants were asked about their familiarity with LMSs and whether they used the Arabic versions of these systems. They were also asked about their experiences with the Arabic localization of these systems and whether they faced any problems in dealing with the Arabic version. The findings indicate that translation inconsistencies are the main problems with the Arabic versions of different LMSs including Blackboard Learn, Microsoft Teams, and Zoom. These problems have negative impacts on the effectiveness and reliability of these systems in schools, universities, and training institutions. For the proper implementation of LMSs both localization and translation should go hand-in-hand. Localization developers and LMSs designers need to consider the linguistic the peculiar linguistic features of Arabic. The findings of the study have implications to translation programs in Arab universities and training institutions. Program designers should integrate translation technologies and localization systems into translation studies. They need to consider the changes within the translation industry. The study was limited to the study of translation quality in the Arabic versions of the localized LMSs. The localization quality of other software programs, games, websites, and applications needs to be explored. Finally, it is recommended to develop a quality matrix that encompasses all the dimensions and peculiarities of Arabic localization.

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“…The paper describes a method that allows finding modifications in digitized (scanned) images of business document pages, while the comparison of the main objects of the documentwordsis based on a combination of comparison methods using character recognition and adaptive pixel-by-pixel comparison. Not only scanned images of documents can be considered but also images that were obtained using mobile camera if the document was localized in the image, for example, as in [2]. For noisy images, the method of numerical reconstruction [3] by using regularization algorithms can be used.…”

Section: Introductionmentioning

confidence: 99%

Comparison of scanned administrative document images

Андреева¹,

Arlazarov²,

Slavin

et al. 2020

Twelfth International Conference on Machine Vision (ICMV 2019)

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In this work the methods of comparison of digitized copies of administrative documents were considered. This problem arises, for example, when comparing two copies of documents signed by two parties in order to find possible modifications made by one party, in the banking sector at the conclusion of contracts in paper form. The proposed method of document image comparison is based on a combination of several ways of image comparison of words that are descriptors of text feature points. Testing was conducted on public Payslip Dataset (French). The results showed the high quality and the reliability of finding differences in two images that are versions of the same document.

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Document localization algorithms based on feature points and straight lines

Cited by 17 publications

References 3 publications

Fast projective image rectification for planar objects with Manhattan structure

Fast projective image rectification for planar objects with Manhattan structure

An Evaluation of the Localization Quality of the Arabic Versions of Learning Management Systems

Comparison of scanned administrative document images

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