Using Special Text Points in the Recognition of Documents

Slavin, Oleg

doi:10.1007/978-3-030-32579-4_4

Cited by 14 publications

(2 citation statements)

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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%

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%

Document image analysis and recognition: a survey

et al. 2022

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“…Flexible form models are based on text segmentation and recognition result analysis and describe documents with soft restrictions on their structure. This model may contain text feature points [32] or attributed relational graphs [33] as a structural representation of a document. End-to-end models involve the simultaneous segmentation and recognition of text field regions [34] and may not require any document 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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