Отрывок: It contains manuscripts written in modern German. Train sample consists of 353 lines, validation - 29 lines, and test - 87 lines. Schiller contains handwritten texts written in modern German. Train sample consists of 244 lines, validation - 21 lines, and test - 63 lines. Ricordi contains handwritten texts written in Italian. Train sample consists of 295 lines, validation - 19 lines, and test - 69 lines. http://www.computeroptics.ru/eng/index.html journal@computeroptics.ru...
Название : | Handwritten text generation and strikethrough characters augmentation |
Авторы/Редакторы : | Shonenkov, A.V. Karachev, D.K. Novopoltsev, M.Y. Potanin, M.S. Dimitrov, D.V. Chertok, A.V. |
Ключевые слова : | data augmentation handwritten text recognition strikethrough text computer vision StackMix handwritten blots |
Дата публикации : | Июн-2022 |
Издательство : | Самарский национальный исследовательский университет |
Библиографическое описание : | Shonenkov AV, Karachev DK, Novopoltsev MY, Potanin MS, Dimitrov DV, Chertok AV. Handwritten text generation and strikethrough characters augmentation. Computer Optics 2022; 46(3): 455-464. DOI: 10.18287/2412-6179-CO-1049. |
Серия/номер : | 46;3 |
Аннотация : | We introduce two data augmentation techniques, which, used with a Resnet-BiLSTM-CTC network, significantly reduce Word Error Rate and Character Error Rate beyond best-reported results on handwriting text recognition tasks. We apply a novel augmentation that simulates strikethrough text (HandWritten Blots) and a handwritten text generation method based on printed text (StackMix), which proved to be very effective in handwriting text recognition tasks. StackMix uses weakly-supervised framework to get character boundaries. Because these data augmentation techniques are independent of the network used, they could also be applied to enhance the performance of other networks and approaches to handwriting text recognition. Extensive experiments on ten handwritten text datasets show that HandWritten Blots augmentation and StackMix significantly improve the quality of handwriting text recognition models. |
URI (Унифицированный идентификатор ресурса) : | https://dx.doi.org/10.18287/2412-6179-CO-1049 http://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/Handwritten-text-generation-and-strikethrough-characters-augmentation-103043 |
Другие идентификаторы : | Dspace\SGAU\20230413\103043 Dspace\SGAU\20230426\103043 Dspace\SGAU\20230503\103043 |
Располагается в коллекциях: | Журнал "Компьютерная оптика" |
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2412-6179_2022_46-3_455-464.pdf | Основная статья | 1.32 MB | Adobe PDF | Просмотреть/Открыть |
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