Data Extraction from Similarly Structured Scanned Documents
https://doi.org/10.26907/1562-5419-2021-24-4-667-688
Abstract
Currently, the major part of transmitted and stored data is unstructured, and the amount of unstructured data is growing rapidly each year, although it is hardly searchable, unqueryable, and its processing is not automated. At the same time, there is a growth of electronic document management systems. This paper proposes a solution for extracting data from paper documents considering their structure and layout based on document photos. By examining different approaches, including neural networks and plain algorithmic methods, we present their results and discuss them.
About the Authors
R. D. Saitgareev
Kazan (Volga region) Federal University
Russian Federation
Russian Federation
B. R. Giniyatullin
Kazan (Volga region) Federal University
Russian Federation
Russian Federation
V. Y. Toporov
Kazan (Volga region) Federal University
Russian Federation
Russian Federation
A. A. Atnagulov
Kazan (Volga region) Federal University
Russian Federation
Russian Federation
F. R. Aglyamov
Kazan (Volga region) Federal University
Russian Federation
Russian Federation
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Review
For citations:
Saitgareev R.D., Giniyatullin B.R., Toporov V.Y., Atnagulov A.A., Aglyamov F.R. Data Extraction from Similarly Structured Scanned Documents. Russian Digital Libraries Journal. 2021;24(4):667-688. (In Russ.) https://doi.org/10.26907/1562-5419-2021-24-4-667-688
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