Maximal coordinate discrepancy as accuracy criterion of image projective normalization for optical recognition of documents
Автор: Konovalenko I.A., Kokhan V.V., Nikolaev D.P.
Рубрика: Программирование
Статья в выпуске: 3 т.13, 2020 года.
Бесплатный доступ
Application of projective normalization (a special case of orthocorrection and perspective correction) to photographs of documents for their further optical recognition is generally accepted. In this case, inaccuracies of normalization can lead to recognition errors. To date, a number of normalization accuracy criteria are presented in the literature, but their conformity with recognition quality was not investigated. In this paper, for the case of a fixed structured document, we justify a uniform probabilistic model of recognition errors, according to which the probability of correct recognition of a character abruptly falls to zero with an increase in the coordinate discrepancy of this character. For this model, we prove that the image normalization accuracy criterion, which is equal to the maximal coordinate discrepancy in the text fields of a document, monotonously depends on the probability of correct recognition of the entire document. Also, we show that the problem on computing the maximal coordinate discrepancy is not reduced to the nearest known one, i.e. the linear-fractional programming problem. Finally, for the first time, we obtain an analytical solution to the problem on computing the maximal coordinate discrepancy on a union of polygons.
Orthocorrection, perspective correction, image projective normalization, optical character recognition, accuracy criteria, coordinate discrepancy, nonlinear programming
Короткий адрес: https://sciup.org/147235021
IDR: 147235021 | УДК: 004.932.2 | DOI: 10.14529/mmp200304
Максимальная невязка координат как критерий точности проективной нормализации изображения при оптическом распознавании документов
Общепринято применение проективной нормализации (частный случай ортокоррекции и коррекции перспективы) к фотографиям документов для их последующего оптического распознавания. При этом неточности нормализации могут приводить к ошибкам распознавания. На сегодняшний день в литературе предложен ряд критериев точности нормализации, однако их соответствие качеству распознавания не исследуется. В данной работе для случая документа фиксированной структуры обосновывается равномерная вероятностная модель ошибок распознавания, в соответствии с которой вероятность верного распознавания символа скачком падает до нуля с ростом невязки координат этого символа. Для этой модели доказано, что критерий точности нормализации изображения, равный максимальной по текстовым полям документа невязке координат, монотонно связан с вероятностью верного распознавания всего документа. Показано, что задача вычисления максимальной невязки координат не сводится к ближайшей известной, т.е. задаче дробно-линейного программирования. Наконец, впервые получено аналитическое решение задачи вычисления максимальной невязки координат на объединении многоугольников.
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