Změny mezi verzí 19 a verzí 20 u OcrDataset
- Časová značka:
- 28. 11. 2022 13:13:12 (před 20 měsíci)
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OcrDataset
v19 v20 7 7 The dataset from 2021 is structured as follows: 8 8 9 * The archive [https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-4615/scanned-images.zip?sequence=7&isAllowed=y scanned-images.zip](47.13 GB) contains 51,351 high-resolution scanned images.10 * The archive [https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-4615/ocr-texts.zip?sequence=5&isAllowed=y ocr-texts.zip] (5.09 GB)contains 51,351 OCR texts in three formats:9 * The archive [https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-4615/scanned-images.zip?sequence=7&isAllowed=y scanned-images.zip] (47.13 GB) contains 51,351 high-resolution scanned images. 10 * The archive [https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-4615/ocr-texts.zip?sequence=5&isAllowed=y ocr-texts.zip] (5.09 GB) contains 51,351 OCR texts in three formats: 11 11 1. HOCR documents from the Tesseract 4 OCR engine. 12 12 1. JSON documents from the [https://cloud.google.com/vision Google Vision AI] OCR engine. 13 13 1. TXT documents that combine Tesseract and Google outputs to achieve maximum accuracy on different types of layout. 14 * The archive [https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-4615#file_file_7686 annotations-ocr.zip] (178.62 KB)contains 120 annotations for the evaluation of OCR.[[BR]]The archive is divided into two subdirectories for the evaluation of layout analysis:14 * The archive [https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-4615#file_file_7686 annotations-ocr.zip] (178.62 KB) contains 120 annotations for the evaluation of OCR.[[BR]]The archive is divided into two subdirectories for the evaluation of layout analysis: 15 15 1. The subdirectory `with-columns` contains annotations for 17 multi-column pages. 16 16 1. The subdirectory `without-columns` contains annotations for 103 single-column pages. 17 * The archive [https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-4615/annotations-language-identification.zip?sequence=3&isAllowed=y annotations-language-identification.zip] (1.1 MB)contains 122 annotations for the evaluation of language identification.17 * The archive [https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-4615/annotations-language-identification.zip?sequence=3&isAllowed=y annotations-language-identification.zip] (1.1 MB) contains 122 annotations for the evaluation of language identification. 18 18 19 19 The supplementary materials from 2022 are structured as follows: 20 20 21 * The archive [https://nlp.fi.muni.cz/projects/ahisto/ocr-texts-supplementary.zip ocr-texts-supplementary.zip] (24.39 MB) contains 110 OCR texts for which we have both high-resolution scanned images and also annotations for the evaluation of OCR.[[BR]]The archive is divided into a number of subdirectories with outputs of different OCR engines:21 * The archive [https://nlp.fi.muni.cz/projects/ahisto/ocr-texts-supplementary.zip ocr-texts-supplementary.zip] (24.39 MB) contains 110 OCR texts for which we have both high-resolution scanned images and annotations for OCR evaluation.[[BR]]The archive is divided into a number of subdirectories with outputs of different OCR engines: 22 22 * The subdirectory `google-vision-ai-old` contains JSON and TXT documents from the Google Vision AI OCR engine from 2020-10-02. 23 23 * The subdirectory `google-vision-ai` contains JSON and TXT documents from the Google Vision AI OCR engine from 2022-08-11. … … 32 32 If you use our dataset in your work, please cite the following article: 33 33 34 Novotný, V., Seidlová, K., Vrabcová, T., Horák, A.: When Tesseract Brings Friends: Layout Analysis, Language Identification, and Super-Resolution in the Optical Character Recognition of Medieval Texts. In: Horák, A., Rychlý, P., Rambousek, A. (eds.) '' Proceedings of Recent Advances in Slavonic Natural Language Processing, RASLAN 2021''. pp. 91–100. ISSN 2336-4289. ISBN 978-80-263-1600-8. Tribun EU (2021). Available also from WWW: https://nlp.fi.muni.cz/raslan/2021/paper10.pdf34 Novotný, V., Seidlová, K., Vrabcová, T., Horák, A.: When Tesseract Brings Friends: Layout Analysis, Language Identification, and Super-Resolution in the Optical Character Recognition of Medieval Texts. In: Horák, A., Rychlý, P., Rambousek, A. (eds.) '' Proceedings of Recent Advances in Slavonic Natural Language Processing, RASLAN 2021'' . pp. 91–100. ISSN 2336-4289. ISBN 978-80-263-1600-8. Tribun EU (2021). Available also from WWW: https://nlp.fi.muni.cz/raslan/2021/paper10.pdf 35 35 36 36 If you use LaTeX, you can use the following BibTeX entry: