Document image analysis and recognition: a survey

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.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

Document image analysis and recognition: a survey

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

Development of Polynomial Regression in recognition of printed and hand-printed letters

Abstract: Recognition processes for printed and hand-printed character images are discussed. Theoretical and practical aspects of Polynomial Regression are considered. The results of program realization are presented and compared to other methods (neural network approach, algorithm of comparison with etalons).Note: Research direction:Mathematical problems and theory of numerical method

European guidelines for primary antifungal prophylaxis in adult haematology patients: summary of the updated recommendations from the European Conference on Infections in Leukaemia

Contains fulltext : 200092.pdf (publisher's version ) (Closed access)The European Conference on Infections in Leukaemia (ECIL) updated its guidelines on antifungal prophylaxis for adults using the grading system of IDSA. The guidelines were extended to provide recommendations for other haematological diseases besides AML and recipients of an allogeneic haematopoietic stem cell transplantation (HSCT). Posaconazole remains the drug of choice when the incidence of invasive mould diseases exceeds 8%. For patients undergoing remission-induction chemotherapy for AML and myelodysplastic syndrome (MDS), fluconazole can still offer an alternative provided it forms part of an integrated care strategy that includes screening with biomarkers and imaging. Similarly, aerosolized liposomal amphotericin B combined with fluconazole can be considered for patients at high risk of invasive mould diseases but other formulations of the polyene are discouraged. Fluconazole is still recommended as primary prophylaxis for patients at low risk of invasive mould diseases during the pre-engraftment phase of allogeneic HSCT whereas only a moderate recommendation could be made for itraconazole, posaconazole and voriconazole for patients at high risk. Posaconazole is strongly recommended for preventing invasive mould disease post-engraftment but only when graft-versus-host disease (GvHD) was accompanied by other risk factors such as its severity, use of an alternative donor or when unresponsive to standard corticosteroid therapy. The need for primary prophylaxis for other patient groups was less clear and should be defined by the estimated risk of invasive fungal disease (IFD)

ESCMID* guideline for the diagnosis and management of Candida diseases 2012: developing European guidelines in clinical microbiology and infectious diseases.

The process to develop a guideline in a European setting remains a challenge. The ESCMID Fungal Infection Study Group (EFISG) successfully achieved this endeavour. After two face-to-face meetings, numerous telephone conferences, and email correspondence, an ESCMID task force (basically composed of members of the Society's Fungal Infection Study Group, EFISG) finalized the ESCMID diagnostic and management/therapeutic guideline for Candida diseases. By appreciating various patient populations at risk for Candida diseases, four subgroups were predefined, mainly ICU patients, paediatric, HIV/AIDS and patients with malignancies including haematopoietic stem cell transplantation. Besides treatment recommendations, the ESCMID guidelines provide guidance for diagnostic procedures. For the guidelines, questions were formulated to phrase the intention of a given recommendation, for example, outcome. The recommendation was the clinical intervention, which was graded by a score of A-D for the 'Strength of a recommendation'. The 'level of evidence' received a score of I-III. The author panel was approved by ESCMID, European Organisation for Research and Treatment of Cancer, European Group for Blood and Marrow Transplantation, European Society of Intensive Care Medicine and the European Confederation of Medical Mycology. The guidelines followed the framework of GRADE and Appraisal of Guidelines, Research, and Evaluation. The drafted guideline was presented at ECCMID 2011 and points of discussion occurring during that meeting were incorporated into the manuscripts. These ESCMID guidelines for the diagnosis and management of Candida diseases provide guidance for clinicians in their daily decision-making process