Dynamic programming approach to template-based OCR

Povolotskiy, Mikhail A.; Tropin, Daniil V.

doi:10.1117/12.2522974

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Based on these components, such approaches restore the overall structure. Bottom-up group includes methods based on groups of connected components analysis, line segments identification [71], morphological image analysis for text components search [72], textual key points and relations between them [73], and methods based on Voronoi diagrams and Delaunay triangulation.…”

Section: Document Structure Analysis Algorithmsmentioning

confidence: 99%

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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: Document Structure Analysis Algorithmsmentioning

confidence: 99%

“…In [125], the problem of a composite object segmentation with known constraints on the mutual arrangement of its elements is considered. In [72], the case when the constraint graph is a simple chain is considered, and by means of dynamic programming, the search of field boundaries for IDs and license plates is performed.…”

Section: Extraction Of Attributesmentioning

confidence: 99%

Document image analysis and recognition: a survey

et al. 2022

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“…The method of segmentation could vary from zone to zone, depending on its structure: some zones could have fixed local coordinates of each individual object or field, specified in its configuration, and thus no additional search is required. Other zones may require a search of precise field coordinates with respect to a predefined pattern of fields [79], or even an application of a free-form text detection [80]. For the latter cases, similar quality metrics could be applied as for the task of document template location, such as Jaccard distance and other Intersection-Over-Union-based metrics [81] or, for independent analysis of text fields detection given their OCR results (see below), characterlevel metrics such as TedEval [82] or CLEval [83].…”

Section: C) Template Processingmentioning

confidence: 99%

Towards a unified framework for identity documents analysis and recognition

Bulatov¹,

Bezmaternykh²,

Nikolaev

et al. 2022

Computer Optics

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Identity documents recognition is far beyond classical optical character recognition problems. Automated ID document recognition systems are tasked not only with the extraction of editable and transferable data but with performing identity validation and preventing fraud, with an increasingly high cost of error. A significant amount of research is directed to the creation of ID analysis systems with a specific focus for a subset of document types, or a particular mode of image acquisition, however, one of the challenges of the modern world is an increasing demand for identity document recognition from a wide variety of image sources, such as scans, photos, or video frames, as well as in a variety of virtually uncontrolled capturing conditions. In this paper, we describe the scope and context of identity document analysis and recognition problem and its challenges; analyze the existing works on implementing ID document recognition systems; and set a task to construct a unified framework for identity document recognition, which would be applicable for different types of image sources and capturing conditions, as well as scalable enough to support large number of identity document types. The aim of the presented framework is to serve as a basis for developing new methods and algorithms for ID document recognition, as well as for far more heavy challenges of identity document forensics, fully automated personal authentication and fraud prevention.

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“…Templates define the strictest constraints on the location of each zone and are most commonly used for identity documents. In [25,29,30], document templates are used for the localization and classification of document images, but they are also helpful in field segmentation [31]. Flexible form models are based on text segmentation and recognition result analysis and describe documents with soft restrictions on their structure.…”

Section: Document Image Quality Assessment Problem Statementmentioning

confidence: 99%

A Method of Image Quality Assessment for Text Recognition on Camera-Captured and Projectively Distorted Documents

Shemiakina¹,

Limonova

Skoryukina³

et al. 2021

Mathematics

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In this paper, we consider the problem of identity document recognition in images captured with a mobile device camera. A high level of projective distortion leads to poor quality of the restored text images and, hence, to unreliable recognition results. We propose a novel, theoretically based method for estimating the projective distortion level at a restored image point. On this basis, we suggest a new method of binary quality estimation of projectively restored field images. The method analyzes the projective homography only and does not depend on the image size. The text font and height of an evaluated field are assumed to be predefined in the document template. This information is used to estimate the maximum level of distortion acceptable for recognition. The method was tested on a dataset of synthetically distorted field images. Synthetic images were created based on document template images from the publicly available dataset MIDV-2019. In the experiments, the method shows stable predictive values for different strings of one font and height. When used as a pre-recognition rejection method, it demonstrates a positive predictive value of 86.7% and a negative predictive value of 64.1% on the synthetic dataset. A comparison with other geometric quality assessment methods shows the superiority of our approach.

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Dynamic programming approach to template-based OCR

Cited by 6 publications

References 9 publications

Document image analysis and recognition: a survey

Document image analysis and recognition: a survey

Towards a unified framework for identity documents analysis and recognition

A Method of Image Quality Assessment for Text Recognition on Camera-Captured and Projectively Distorted Documents

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