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Table Recognition Technology in Tax Documents of the Russian Federation

Authors: Slavin, O.A.;

Table Recognition Technology in Tax Documents of the Russian Federation

Abstract

Олег Анатольевич Славин, доктор технических наук, доцент, главный научный сотрудник, федеральный исследовательский центр ≪Информатика и управление≫ РАН (г. Москва, Российская Федерация); старший научный сотрудник-программист, ООО ≪Смарт Энджинс Сервис≫ (г. Москва, Российская Федерация), oslavin@isa.ru. This paper investigates the problem of cell recognition in the image of a table using the example of the Russian tax document (2-NDFL). Despite the simple structure of the tables, the printing method is based on a flexible template. The flexibility of the form is observed in the modifications of textual information and in the table area. The flexibility of tables lies in the modification of the number and size of columns. A structural method was proposed for table detection. The input data are the detected horizontal and vertical segments. Segments were searched by the Smart Document Reader system. Implementing and testing the method were also carried out in the Smart Document Reader system. In addition to detecting the area where tables can be placed, the following objectives were achieved: searching for table cells, naming table cells, and validating the table area. Validation of the table area was performed for separate tables and for table sets. The application of table aggregate descriptions showed the high reliability of linking table sets. Рассматривается известная задача распознавания ячеек таблиц на изображении. Исследуется обработка налогового российского документа 2-НДФЛ. Несмотря на простую структуру таблиц, способ печати основан на гибком шаблоне. Гибкость формы наблюдается как в части модификаций текстовой информации, так и в области таблиц. Гибкость таблиц состоит в изменении числа и размеров столбцов. Для детектирования таблиц был предложен структурный метод. Входными данными метода являются детектированные горизонтальные и вертикальные отрезки. Поиск отрезков проводился механизмами, реализованными в системе Smart Document Reader. Апробация и внедрение предложенного метода также осуществлялось в системе Smart Document Reader. Кроме детектирования области предполагаемого размещения таблиц решены следующие задачи: поиск ячеек таблиц, именование ячеек таблиц, валидация области таблицы. Валидация области таблицы проводилась для отдельных таблиц, а также для совокупностей таблиц. Применение описаний совокупностей таблиц обеспечило высокую надежность привязки набора таблиц.

Country
Russian Federation
Keywords

раскладка таблиц, line detection, УДК 004.932.72’1, детектирование отрезка, table layout, table recognition, распознавание таблиц

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green
gold