Artificial intelligence and machine learning

Recognition of cadastral coordinates using convolutional recurrent neural networks

I. V. Vinokurov

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

Abstract: The article examines the use of convolutional recurrent neural networks (CRNN) for recognizing images of cadastral coordinates of objects on scanned documents of the «Roskadastr» PLC. The combined CRNN architecture, combining convolutional neural networks (CNN) and recurrent neural networks (RNN), allows you to take advantage of each of them for image processing and recognition of continuous digital sequences contained in them. During experimental studies, images consisting of a given number of digits were generated, and a CRNN model was built and studied. The formation of images of digital sequences consisted of preprocessing and concatenation of images of the digits forming them from one’s own data set. Analysis of the values of the loss function and Accuracy, Character Error Rate (CER) and Word Error Rate (WER) metrics showed that the use of the proposed CRNN model makes it possible to achieve high accuracy in recognizing cadastral coordinates in their scanned images.

Key words and phrases: convolutional recurrent neural network, CRNN, image recognition, digital sequences, deep learning, Keras, Python.

UDC: 004.932.75'1, 004.89
BBK: 32.813.5: 32.973.202-018.2

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

Received: 29.09.2023
Accepted: 27.11.2023

Language: Russian and English

DOI: 10.25209/2079-3316-2024-15-1-3-30