Document Detection in Videos Captured by Smartphones using a Saliency-Based Method

Ngoc, Minh; Fabrizio, Jonathan; Géraud, Thierry

doi:10.1109/icdarw.2019.30059

Cited by 8 publications

(5 citation statements)

References 28 publications

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“…Currently, capturing document images with a smartphone camera or webcam [148] seems preferable from the user's point of view compared to using scanners. From the developer's point of view, by contrast, the mobile document recognition mode is much more complex.…”

Section: Video Sequence Recognitionmentioning

confidence: 99%

Document image analysis and recognition: a survey

et al. 2022

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This paper analyzes the problems of document image recognition and the existing solutions. Document recognition algorithms have been studied for quite a long time, but despite this, currently, the topic is relevant and research continues, as evidenced by a large number of associated publications and reviews. However, most of these works and reviews are devoted to individual recognition tasks. In this review, the entire set of methods, approaches, and algorithms necessary for document recognition is considered. A preliminary systematization allowed us to distinguish groups of methods for extracting information from documents of different types: single-page and multi-page, with text and handwritten contents, with a fixed template and flexible structure, and digitalized via different ways: scanning, photographing, video recording. Here, we consider methods of document recognition and analysis applied to a wide range of tasks: identification and verification of identity, due diligence, machine learning algorithms, questionnaires, and audits. The groups of methods necessary for the recognition of a single page image are examined: the classical computer vision algorithms, i.e., keypoints, local feature descriptors, Fast Hough Transforms, image binarization, and modern neural network models for document boundary detection, document classification, document structure analysis, i.e., text blocks and tables localization, extraction and recognition of the details, post-processing of recognition results. The review provides a description of publicly available experimental data packages for training and testing recognition algorithms. Methods for optimizing the performance of document image analysis and recognition methods are described.

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Section: Video Sequence Recognitionmentioning

confidence: 99%

Document image analysis and recognition: a survey

et al. 2022

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“…Along with the applicability of the text recognition technologies, the requirements for the quality and reliability of recognition results are increasing [10]. 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 [11,12].…”

Section: Document Recognition In Uncontrolled Conditionsmentioning

confidence: 99%

“…It can also be noticed that, on average, the best result is achieved by a weighted combination of the best 50 % of frames. Figures 11,12,and 13 show comparative profiles for combining the best 50 % frames for different combining strategies. Tab.…”

Section: Performance Profiles For Weighted Combination Based On Focus...mentioning

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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“…The second approach to contrast analysis can be conventionally referred to as proceeding from the particular to the general: the position of the document is evaluated based on the segmentation into two classes (document, background) image. Ngoc et al in their work [15] represent the image as an unordered tree of regions, which is obtained by hierarchical segmentation of the image. For segmentation, they use several contrast features at once: the Dahu pseudo distance, the Chisquare distance between histograms of the color sets of pixels.…”

Section: Introductionmentioning

confidence: 99%

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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Document Detection in Videos Captured by Smartphones using a Saliency-Based Method

Cited by 8 publications

References 28 publications

Document image analysis and recognition: a survey

Document image analysis and recognition: a survey

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

Advanced Hough-based method for on-device document localization

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