This article is cited in 2 papers

Artificial Intelligence, Intelligent Systems, Neural Networks

Tabular information recognition using convolutional neural networks

I. V. Vinokurov

Financial University under the Government of the Russian Federation, Moscow, Russia

Abstract: The relevance of identifying tabular information and recognizing its contents for processing scanned documents is shown. The formation of a data set for training, validation and testing of a deep learning neural network (DNN) YOLOv5s for the detection of simple tables is described. The effectiveness of using this DNN when working with scanned documents is shown. Using the Keras Functional API, a convolutional neural network (CNN) was formed to recognize the main elements of tabular information— numbers, basic punctuation marks and Cyrillic letters. The results of a study of the work of this CNN are given. The implementation of the identification and recognition of tabular information on scanned documents in the developed IS updating information in databases for the Unified State Register of Real Estate system is described.

Key words and phrases: Convolutional Neural Networks, Deep Learning Neural Networks, CNN, DNN, YOLOv5s, Keras, Python.

UDC: 004.932.75’1+004.89

MSC: Primary 68T20; Secondary 68T07, 68T45

Received: 23.11.2022
28.11.2022
Accepted: 12.12.2022

Language: Russian and English

DOI: 10.25209/2079-3316-2023-14-1-3-30