Real time rectangular document detection on mobile devices

Skoryukina, Natalya; Nikolaev, Dmitry P.; Sheshkus, Alexander; Polevoy, Dmitry

doi:10.1117/12.2181377

Cited by 27 publications

(27 citation statements)

References 3 publications

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“…In [8], Tropin et al employed triples of straight lines and the reconstructed side to form a quadrilateral in addition to the standard method which searches for 4 straight lines. To reconstruct the fourth side of the document from the detected three, the aspect ratio of the document and the internal parameters of the camera, the principal point position and the focal length, should be known.…”

Section: Reconstruction Of the Document Border Based On Three Lines And Document's Aspect Ratiomentioning

confidence: 99%

“…The Hough-based methods employ contour ranking of the quadrilaterals. This score is based on the integral characteristics of the contours along the sides {b} of the quadrilateral q: the intensity of the edges inside w and outside w of the borders [8], and the shares of non-zero pixels c on the edge map along borders b [5]. In [10], Tropin et al suggested the following contour score:…”

Section: Ranking Quadrilaterals By Contours and Contrastsmentioning

confidence: 99%

“…To evaluate the system's performance when localizing the quadrilateral of the outer boundaries of the document, the metrics of quality based on the maximum discrepancy between the vertices are used [8,9]. The index introduced by Tropin et al in [9] is the best way to evaluate the system in our case.…”

Section: Problems and Corresponding Metricsmentioning

confidence: 99%

“…The systems proposed in [7,8] do not have reference performance evaluations on open datasets. The methods [10,11] were evaluated on the open dataset SmartDoc [23].…”

Section: Introductionmentioning

confidence: 99%

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Advanced Hough-based method for on-device document localization

et al. 2021

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The demand for on-device document recognition systems increases in conjunction with the emergence of more strict privacy and security requirements. In such systems, there is no data transfer from the end device to a third-party information processing servers. The response time is vital to the user experience of on-device document recognition. Combined with the unavailability of discrete GPUs, powerful CPUs, or a large RAM capacity on consumer-grade end devices such as smartphones, the time limitations put significant constraints on the computational complexity of the applied algorithms for on-device execution. In this work, we consider document location in an image without prior knowledge of the docu-ment content or its internal structure. In accordance with the published works, at least 5 systems offer solutions for on-device document location. All these systems use a location method which can be considered Hough-based. The precision of such systems seems to be lower than that of the state-of-the-art solutions which were not designed to account for the limited computational resources. We propose an advanced Hough-based method. In contrast with other approaches, it accounts for the geometric invariants of the central projection model and combines both edge and color features for document boundary detection. The proposed method allowed for the second best result for SmartDoc dataset in terms of precision, surpassed by U-net like neural network. When evaluated on a more challenging MIDV-500 dataset, the proposed algorithm guaranteed the best precision compared to published methods. Our method retained the applicability to on-device computations.

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Section: Reconstruction Of the Document Border Based On Three Lines And Document's Aspect Ratiomentioning

confidence: 99%

Section: Ranking Quadrilaterals By Contours and Contrastsmentioning

confidence: 99%

Section: Problems and Corresponding Metricsmentioning

confidence: 99%

“…The systems proposed in [7,8] do not have reference performance evaluations on open datasets. The methods [10,11] were evaluated on the open dataset SmartDoc [23].…”

Section: Introductionmentioning

confidence: 99%

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Advanced Hough-based method for on-device document localization

et al. 2021

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“…We (4) The resulting output of the algorithm should be a pair of orthogonal vanishing point. Thus in this Section we construct the set of probable candidate points from which a pair could be chosen.…”

Section: Vanishing Points Detection On the Image Planementioning

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)

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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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MobiScan: An enhanced invisible screen‐camera communication system for IoT applications

Xiao

Liu

et al. 2020

Trans Emerging Tel Tech

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In recent years, dynamic and invisible screen‐camera short‐range communication for IoT devices have become a popular research field due to its superior user experience and no‐extra hardware required. However, most existing screen‐camera communication systems suffer from poor data security, high computational overhead, and limited capture angle, which make them infeasible in practice. In this work, we propose MobiScan, a dynamic and invisible screen‐to‐camera communication system that is able to ensure data security, real‐time communication, and flexible capture angle. MobiScan is composed of two parts: a novel fast frame correction scheme and a multilevel data pattern scheme to address the above problems. The fast frame correction scheme proposes a novel frame correct approach that utilizes the sensor data on the smartphone to correct the captured frame by reproducing the relationship between positions of the screen and the smartphone. Through this method, the scheme can save frame correction time without calculating frame content to identify the degree of deformation. The multilevel data pattern scheme protects the data privacy by sending the classified information to targeted users, which can reduce the time overhead and extend the application scenario by redesigning the data structure. The experimental results show that for flexible capture angle problem, the maximum capture angle is 180° in the rotate view and 60° in the side view, bottom view, and overlook view. For real‐time communication problem, the time overhead is reduced by 90% in the procedure of the frame correction. In the data decoding process, the time overhead is reduced by 10%. MobiScan enables a flexible capture angle on the PC platform for secure and real‐time communication.

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Real time rectangular document detection on mobile devices

Cited by 27 publications

References 3 publications

Advanced Hough-based method for on-device document localization

Advanced Hough-based method for on-device document localization

Fast projective image rectification for planar objects with Manhattan structure

MobiScan: An enhanced invisible screen‐camera communication system for IoT applications

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