innovationИнформация и инновацииInformation and Innovations1994-24432949-2157МЦНТИ10.31432/1994-2443.2025.11innovation-307Research ArticleИНФОРМАЦИОННЫЕ ПРОЦЕССЫInformation processesРазработка прототипа системы распознавания и классификации корпоративных документовDevelopment of a prototype system for recognizing and classifying corporate documentshttps://orcid.org/0009-0001-1436-9621ПерловИ. В.PerlovI. V.

Иван Владимирович Перлов

проспект Вернадского, 78, г. Москва, 119454

Ivan V. Perlov

78, Vernadsky Avenue, Moscow, 119454

perlovivan@yandex.ruhttps://orcid.org/0000-0002-1229-9025СеливановС. А.SelivanovS. A.

Сергей Александрович Селиванов, канд. техн. наук, доцент

проспект Вернадского, 78, г. Москва, 119454

Sergey A. Selivanov, Cand. Sci. (Eng.), Associate Professor

78, Vernadsky Avenue, Moscow, 119454

elivanov@inevm.ruhttps://orcid.org/0000-0001-7392-1837СиницынА. В.SinitsynA. V.

Александр Владимирович Синицын, канд. физ.-мат. наук

проспект Вернадского, 78, г. Москва, 119454

Alexander V. Sinitsyn, PhD of Physico-Mathematical Sciences, Associate Professor

78, Vernadsky Avenue, Moscow, 119454

a@sinitsyn.infohttps://orcid.org/0009-0002-0805-0742ШахгусейновШ. М.ShakhguseynovSh. M.

Шамхал Мехти оглы Шахгусейнов

проспект Вернадского, 78, г. Москва, 119454

Shamhal M. Shakhguseynov

78, Vernadsky Avenue, Moscow, 119454

boss.shamhal@mail.ruФедеральное государственное бюджетное образовательное учреждение высшего образования «МИРЭА — Российский технологический университет»РоссияFederal State Budgetary Educational Institution of Higher Education “MIREA — Russian Technological University”Russian Federation2025271120252024157Copyright © Перлов И.В., Селиванов С.А., Синицын А.В., Шахгусейнов Ш.М., 20252025Перлов И.В., Селиванов С.А., Синицын А.В., Шахгусейнов Ш.М.Perlov I.V., Selivanov S.A., Sinitsyn A.V., Shakhguseynov S.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://journal.icsti.int/jour/article/view/307

Актуальность. В современных условиях становится важным повышение точности и скорости обработки документов. Цель. Разработка системы конвертации, распознавания и классификации корпоративных документов в нередактируемых форматах.Материалы и методы. В разработке использовался язык программирования Python 3.10, библиотеки scikit-learn 1.6, joblib и poppler, модуль Razdel, PyTorch 2.2, Hugging Face Transformers 4.39. пакеты PyPDF2/pdfminer.six/pdfplumber; инструмент Tesseract OCR 5 с использованием pytesseract. Для устранения разрывов строк и уменьшения шума использовался пакет OpenCV-python. Веб-интерфейс строился на Vite и React с использованием Bootstrap 5.Результаты. Разработан прототип системы, позволяющий эффективно конвертировать документ из нередактируемого формата в редактируемый в форме определенного документа.Выводы. Использование технологий искусственного интеллекта ускоряет рабочие процессы, уменьшает окно ошибок. Решение интегрируется в рабочие процессы, но для обучения классификации требуется большое количество данных

Relevance. In today’s environment, improving the accuracy and speed of document processing is becoming increasingly important.Target. Development of a system for converting, recognizing, and classifying corporate documents in non-editable formats.Materials and Methods. The development utilized Python 3.10, the scikit-learn 1.6 library, joblib and poppler, the Razdel module, PyTorch 2.2, and Hugging Face Transformers 4.39. The PyPDF2 / pdfminer.six / pdfplumber packages; and the Tesseract OCR 5 tool using pytesseract. The OpenCV-python package was used to eliminate line breaks and reduce noise. The web interface was built on Vite and React using Bootstrap 5.Results. A prototype system was developed that enables efficient document conversion from a non-editable format to an editable one within a specific document.Conclusions. The use of artificial intelligence technologies accelerates workflows and reduces the error window. The solution integrates into workflows, but classification training requires a large amount of data

искусственный интеллектизвлечение информацииклассификация документовоптическое распознавание символовизвлечение сущностейавтоматизация документооборотаartificial intelligenceinformation extractiondocument classificationoptical character recognitionnamed entity recognitionautomation of document flowReferencesSu J., Ahmed M., Lu Yu., Pan Sh., Bo W., Liu Yu. RoFormer: Enhanced transformer with Rotary Position Embedding. Neurocomputing. 2024;568:127063. https://doi.org/10.1016/j.neucom.2023.127063Su J., Ahmed M., Lu Yu., Pan Sh., Bo W., Liu Yu. RoFormer: Enhanced transformer with Rotary Position Embedding. Neurocomputing. 2024;568:127063. https://doi.org/10.1016/j.neucom.2023.127063Romero-Fresco P. Subtitling through Speech Recognition: Respeaking. Manchester: St. Jerome, 2011. 261 p. ISBN 9781905763283.Romero-Fresco P. Subtitling through Speech Recognition: Respeaking. Manchester: St. Jerome, 2011. 261 p. ISBN 9781905763283.Park J., Lee E., Kim Y., Kang I., Koo H.I., Cho N.I. Multi-Lingual Optical Character Recognition System Using the Reinforcement Learning of Character Segmenter. IEEE Access. 2020;8:174437-174448. https://doi.org/10.1109/ACCESS.2020.3025769Park J., Lee E., Kim Y., Kang I., Koo H.I., Cho N.I. Multi-Lingual Optical Character Recognition System Using the Reinforcement Learning of Character Segmenter. IEEE Access. 2020;8:174437-174448. https://doi.org/10.1109/ACCESS.2020.3025769Memon J., Sami M., Khan R.A. Handwritten Optical Character Recognition (OCR): Comprehensive Systematic Literature Review (SLR). IEEE Access. 2020;8:142642- 142668. https://doi.org/10.1109/ACCESS.2020.3012542Memon J., Sami M., Khan R.A. Handwritten Optical Character Recognition (OCR): Comprehensive Systematic Literature Review (SLR). IEEE Access. 2020;8:142642- 142668. https://doi.org/10.1109/ACCESS.2020.3012542Hossain A., Ali M. Recognition of Handwritten Digit using Convolutional Neural Network (CNN). Global Journal of Computer Science and Technology. 2019;19(2):27-33. https://doi.org/10.34257/GJCSTDVOL19IS2PG27Hossain A., Ali M. Recognition of Handwritten Digit using Convolutional Neural Network (CNN). Global Journal of Computer Science and Technology. 2019;19(2):27-33. https://doi.org/10.34257/GJCSTDVOL19IS2PG27Wani N., Mangire G., Kumar A., Solse N., Gaikwad P.S. Legal Document Classification using TF-IDF and KNN. International Journal of Advanced Research in Science, Communication and Technology. 2022;2(1):590-595. https://doi.org/10.48175/IJARSCT-7522Wani N., Mangire G., Kumar A., Solse N., Gaikwad P.S. Legal Document Classification using TF-IDF and KNN. International Journal of Advanced Research in Science, Communication and Technology. 2022;2(1):590-595. https://doi.org/10.48175/IJARSCT-7522Nasu Iu., Lanin V.V. Development of Legal Document Classification System Based on Support Vector Machine. Trudy ISP RAN / Proc. ISP RAS. 2023;35(2):49-56. https://doi.org/10.15514/ISPRAS2023-35(2)-4Nasu Iu., Lanin V.V. Development of Legal Document Classification System Based on Support Vector Machine. Trudy ISP RAN / Proc. ISP RAS. 2023;35(2):49-56. https://doi.org/10.15514/ISPRAS2023-35(2)-4Yulianti E., Bhary N., Abdurrohman J., Dwitilas F.W., Nuranti E.Q., Husin H.S. Named entity recognition on Indonesian legal documents: a dataset and study using transformer-based models. International Journal of Electrical and Computer Engineering (IJECE). 2024;14(5):5489-5501. https://doi.org/10.11591/ijece.v14i5.pp5489-5501Yulianti E., Bhary N., Abdurrohman J., Dwitilas F.W., Nuranti E.Q., Husin H.S. Named entity recognition on Indonesian legal documents: a dataset and study using transformer-based models. International Journal of Electrical and Computer Engineering (IJECE). 2024;14(5):5489-5501. https://doi.org/10.11591/ijece.v14i5.pp5489-5501Leitner E., Rehm G., Moreno-Schneider J. Fine-Grained Named Entity Recognition in Legal Documents. Lecture Notes in Computer Science. 2019;11702:272-287. https://doi.org/10.1007/978-3-030-33220-4_20Leitner E., Rehm G., Moreno-Schneider J. Fine-Grained Named Entity Recognition in Legal Documents. Lecture Notes in Computer Science. 2019;11702:272-287. https://doi.org/10.1007/978-3-030-33220-4_20Wadud M.A.H., Mridha M.F., Shin J., Nur K., Saha A.K. Deep-BERT: Transfer Learning for Classifying Multilingual Offensive Texts on Social Media. Comput Syst Sci Eng. 2023;44(2):1775–1791. https://doi.org/10.32604/csse.2023.027841Wadud M.A.H., Mridha M.F., Shin J., Nur K., Saha A.K. Deep-BERT: Transfer Learning for Classifying Multilingual Offensive Texts on Social Media. Comput Syst Sci Eng. 2023;44(2):1775–1791. https://doi.org/10.32604/csse.2023.027841Kalyan K.S., Rajasekharan A., Sangeetha S. AMMU: A survey of transformer-based biomedical pretrained language models. Journal of Biomedical Informatics. 2022 Feb;126:103982. https://doi.org/10.1016/j.jbi.2021.103982Kalyan K.S., Rajasekharan A., Sangeetha S. AMMU: A survey of transformer-based biomedical pretrained language models. Journal of Biomedical Informatics. 2022 Feb;126:103982. https://doi.org/10.1016/j.jbi.2021.103982Al-Askary Y.B., Al-Momen S. Enhanced OCR Techniques for Recognizing Mathematical Expressions in Scanned Documents. Ibn AL-Haitham Journal For Pure and Applied Sciences. 2025;38(4):295–306. https://doi.org/10.30526/38.4.3640Al-Askary Y.B., Al-Momen S. Enhanced OCR Techniques for Recognizing Mathematical Expressions in Scanned Documents. Ibn AL-Haitham Journal For Pure and Applied Sciences. 2025;38(4):295–306. https://doi.org/10.30526/38.4.3640Wang Z., Liu M., Liu K. Utilizing Machine Learning Techniques for Classifying Translated and Non-Translated Corporate Annual Reports. Applied Artificial Intelligence. 2024;38(1):e2340393. https://doi.org/10.1080/08839514.2024.2340393Wang Z., Liu M., Liu K. Utilizing Machine Learning Techniques for Classifying Translated and Non-Translated Corporate Annual Reports. Applied Artificial Intelligence. 2024;38(1):e2340393. https://doi.org/10.1080/08839514.2024.2340393Dong M., Gagnon M-A. Unveiling chemical industry secrets: Insights gleaned from scientific literatures that examine internal chemical corporate documents—A scoping review. PLoS ONE. 2025;20(1):e0310116. https://doi.org/10.1371/journal.pone.0310116Dong M., Gagnon M-A. Unveiling chemical industry secrets: Insights gleaned from scientific literatures that examine internal chemical corporate documents—A scoping review. PLoS ONE. 2025;20(1):e0310116. https://doi.org/10.1371/journal.pone.0310116

The authors declare that there are no conflicts of interest present.