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Document image analysis and recognition: a survey
V.V. Arlazarov 1,2, E.I. Andreeva 2, K.B. Bulatov 1,2, D.P. Nikolaev 3, O.O. Petrova 2, B.I. Savelev 2, O.A. Slavin 1

Federal Research Center "Computer Sciences and Control" Russian Academy of Sciences,
117312, Moscow, Russia, prosp. 60-letiya Oktyabrya, 9;
LLC "Smart Engines Service", 117312, Moscow, Russia, prosp. 60-letiya Oktyabrya, 9;
Federal Publicly Funded Institution of Science, Institute for Information Transmission Problems
n.a. A.A. Kharkevich of Russian Academy of Science, 127051, Moscow, Russia Bolshoy Karetny per. 19

 PDF, 1288 kB

DOI: 10.18287/2412-6179-CO-1020

Pages: 567-589.

Full text of article: English language.

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.

document recognition, image normalization, binarization, local features, segmentation, document boundary detection, artificial neural network, information extraction, document sorting, document comparison, video sequence recognition.

Arlazarov VV, Andreeva EI, Bulatov KB, Nikolaev DP, Petrova OO, Savelev BI, Slavin OA. Document image analysis and recognition: a survey. Computer Optics 2022; 46(4): 567-589. DOI: 10.18287/2412-6179-CO-1020.

The reported study was funded by RFBR, project number 20-17-50177. The authors thank Sc. D. Vladimir L. Arlazarov (FRC CSC RAS), Pavel Bezmaternykh (FRC CSC RAS), Elena Limonova (FRC CSC RAS), Ph. D. Dmitry Polevoy (FRC CSC RAS), Daniil Tropin (LLC "Smart Engines Service"), Yuliya Chernysheva (LLC "Smart Engines Service"), Yuliya Shemyakina (LLC "Smart Engines Service") for valuable comments and suggestions.


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