Recognition of printed and handwritten texts based on the neural networks
https://doi.org/10.33186/1027-3689-2026-4-159-176
Abstract
The authors examine the issues of text machine readability in the age of digital technologies. The optical character recognition (OCR) of printed texts and manuscripts offer solutions. The main tasks of the proposed method is to analyze existing recognition systems and algorithms, own code design and testing for various fonts The authors examine the computer vision function for handwritten and printed text processing. Besides, recognition can be improved by dividing images into the black and white, and highlighting symbol parts. Many systems recognize printed text at low error rate, however recognition of handwriting is a challenge for many global languages. Not every handwritten text recognition system can be applied to printed texts, especially with use of neural networks. Most often, such systems operate with feature- or template-driven methods.
About the Authors
Alexander V. Frolov
Admiral Nevelskoy Maritime State University
Russian Federation
Russian Federation
Alexander V. Frolov – Head, Information Technologies Department,
Vladivostok.
Elena A. Vereshchagina
Far Eastern Federal University
Russian Federation
Russian Federation
Elena A. Vereshchagina – Cand. Sc. (Engineering), Associate Professor, Information Security Department, Institute of Mathematics and Computer Technology,
Vladivostok.
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Review
For citations:
Frolov A.V., Vereshchagina E.A. Recognition of printed and handwritten texts based on the neural networks. Scientific and Technical Libraries. 2026;1(4):159-176. (In Russ.) https://doi.org/10.33186/1027-3689-2026-4-159-176
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