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Tesseract Pdf to Text C Tesseract pdf to text c Continue OpenCV 3.4.12-dev Open Source Computer Vision OCRTesseract класс обеспечивает интерфейс с tesseract-ocr API (v3.02.02) в СЗ. Подробнее... #include виртуальный пустотный пробег <opencv2 ext/ocr.hpp=>(Мат-изображение, std::string No3,output_text, std::вектор < rect=> component_rects No1 <2>-NULL, std::вектор < std::string=> No19,component_texts NULL, std::вектор < float=> No component_confidences-NULL, int component_level'0) CV_OVERRIDE Признать текст с помощью tesseract-ocr API. Больше... виртуальный пустотный пробег (Мат- Изображение, Мат-маска, std::string output_text No <3> <9>, std::вектор < rect=> component_rects No2 <6>-NULL, std::вектор < std::string=> component_texts No05-NULL, std::вектор < float=> No component_confidences-NULL, int component_level'0) CV_OVERRIDE Струнный пробег (InputArray image, int min_confidence, int component_level'0) Струнный запуск (InputArray image, InputArray mask, int min_confidence, int component_level'0) виртуальный набор пустотыWhiteList (const String No char_whitelist)-0 виртуальный «BaseOCR» () класс OCRTesseract предоставляет интерфейс с tesseract-ocr API (v3.02.02) в C. Обратите внимание, что он компилирован только при правильной установке tesseract-ocr. Примечание - создать () статический Ptr<OCRTesseract> cv::text::OCRTesseract::create (const char - datapath , NULL, const char - язык - NULL, const char - char_whitelist - NULL, int oem - OEM_DEFAULT, int psmode - PSM_AUTO ) статический Python:retval-cv.text.OCRTesseract_create (,datapath, language, char_whitelist, oem, psmode) создает экземпляр класса OCRTesseract. Параметры datapaththe имя родительского каталога tessdata закончился с /, или NULL использовать каталог системы по умолчанию. Languagean ISO 639-3 код или NULL будет по умолчанию англ. char_whitelistspecifies символов, используемых для распознавания. NULL по умолчанию 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOP-RSTUVWXY. oemtesseract-ocr предлагает различные режимы двигателя OCR (OEM), по умолчанию tesseract::OEM_DEFAULT используется. Можно посмотреть документацию API tesseract-ocr для других возможных значений. psmodetesseract-ocr предлагает различные режимы сегментации страниц (PSM) tesseract::P SM-AUTO (полностью автоматический анализ макета). Можно посмотреть документацию API tesseract-ocr для других возможных значений. - бег () Виртуальная пустота cv::text:::OCRTesseract::run (Мат и изображение, std::string: output_text, std::component_rects вектор < rect=> std::вектор < std::string=> No component_texts - NULL, std::vector < float=> - component_confidences - NULL, int component_level - 0 ) виртуальный Python:retval- cv.text_OCRTesseract.run (изображение, min_confidence, component_level)retval-cv.text_OCRTesseract.run (изображение, маска, min_confidence, component_level) Распознать текст с tesseract-ocr API. Takes the image to the login and returns the recognized text to output_text settings. Optional also provides Rects for individual elements of the text. (e.g. words) and a list of these textual elements with their values of trust. Image settingsIt CV_8UC1 or CV_8UC3 output_textOutput or tesseract-ocr text. component_rectsIf provided that the method leads to the release of a recte list for individual text items found (such as words or text lines). component_textsIf provided that the method sticks out a list of text lines to recognize found individual text items (such as words or text lines). component_confidencesIf provided that the method sticks out a list of trust values to recognize found individual text items (such as words or text lines). component_levelOCR_LEVEL_WORD (by default), or OCR_LEVEL_TEXTLINE. Implementations cv::text::BaseOCR. - Running () Virtual void cv::text:::::OCRTesseract::run (mat and image, mat and mask, std::string output_text, std::vector'lt; rect; rect-component_rects - NULL, std:::'lt; std:::string'gt; - component_texts - NULL, std::vector'lt; float -component_confidences - NULL, int component_level and 0 ) Virtual Python:retval-cv.text_OCRTesseract.run (image, image, min_confidence, component_level)retval-cv.text_OCRTesseract.run (image, mask, min_confidence, component_level)) int min_confidence, int component_level and 0) Python:retval-cv.text_OCRTesseract.run (image, min_confidence, component_level) retval-cv.text_OCRTesseract.run (image, mask, min_confidence, component_level) Python:retval-cv.text_OCRTesseract.run (image, min_confidence) component_level-cv.text_OCRTesseract.run (image, cv.text_OCRTesseract mask, min_confidence, component_level) - setWhiteList () virtual void cv:::text::OCRTesseract::setWhiteList (const String and char_whitelist) Pure Virtual Python:None'cv.text_OCRTesseract.setWhiteList (char_whitelist) Documentation for this class was created from the following file: TesseractTesseract 4.1.1 Originally written by Ray Smith, Hewlett-Packard, developer (s)GoogleStable release4.1.1 / December 26, 2019; 9 months ago (2019-12-26) Repositorygithub.com/tesseract-ocr/tesseract Written inC and operating systemLinux, Windows and macOS (x86)Available winterface: English recognition: Afrikaans, Albanian, Arabic, Azerbaijani, Basque, Belarusian, Bengal, Bulgarian, Catalan, Czech, Cherokee, Croatian, Danish, Dutch, English, Espanyer Finnish, French, Galician, German, Greek, Hindi, Hungarian, Indonesian, Italian, Japanese, Kannada, Korean, Latvian, Lithuanian, Malayalam, Macedonian, Maltese, Malay, Norwegian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tesseract is an optical character recognition engine for a variety of operating systems. This is free software released under an Apache license. Originally developed by Hewlett-Packard as proprietary software in the 1980s, it was released as an open source in 2005, and the development has been sponsored by Google since 2006. In 2006, Tesseract was considered one of the most accurate open source OCR engines. The History The Tesseract engine was originally developed as proprietary software at Hewlett Packard labs in Bristol, England and Greeley, Colorado, between 1985 and 1994, with some changes made in 1996 for Windows ports, and some migration from C to C in 1998. A lot of code was written in C, and then a few more were written in C. Since then, the whole code has been converted, at least to compilation with compiler C. In the next decade, very little work has been done. It was released as an open source in 2005 by Hewlett Packard and the University of Nevada, Las Vegas (UNLV). The development of Tesseract has been sponsored by Google since 2006. Features Tesseract in 1995 in the top three OCR engines in terms of character accuracy. It is available for Linux, Windows and Mac OS X. However, due to limited resources, it is only thoroughly tested by developers under Windows and Ubuntu. Tesseract before and including version 2 could only take TIFF images of the simple text of a single column as input. These early versions do not include layout analysis, so the input of multicost text, images, or equations has produced a distorted output. Starting with 3.00, Tesseract supports the formatting of the output text, hOCR positional information, and page layout analysis. Support for a number of new image formats has been added through the Leptonica library. Tesseract can determine whether the text is monospace or proportionally blurred. Initial versions of Tesseract could only recognize English-language text. Tesseract v2 added six additional Western languages (French, Italian, German, Spanish, Brazilian Portuguese, Dutch). Version 3 has greatly expanded language support, including ideographic (Chinese and Japanese) and left-left (e.g. Arabic, Hebrew) languages, as well as many other scenarios. New languages included Arabic, Bulgarian, Catalan, Chinese (simplified and traditional), Croatian, Czech, Danish, German (Frakturian), Greek, Finnish, Hebrew, Hindi, Hungarian, Indonesian, Japanese, Korean, Latvian, Lithuanian, Norwegian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak V3.04, released in July 2015, added 39 more language/scenario, bringing the total number of support languages to more than 100. New language codes included: amh (Amharic), asm (Assam), aze_cyrl (Azerbaijan in Cyrillic), bod bod bos (Bosnian), ceb (Cebuano), cym (Welsh), dzo (Dzongkha), fas (Persian), gle (Irish), guj (Gujarati), hat (Gayt and Haitian Creole), iku (Inuktitut), jaw (javanese), kat (Georgian), kat_old (Old Georgian), kaz (Old Georgian), kaz Khm (Central Khmer), Kir (Kyrgyz), Mia (Burma), Nep (Nepal), Ori (Oria), Pan (Panjabi), Puy (Pashto), San (Sanskrit), Sin (Sinhala), srp_latn (Serbian in Latin), Sir (Syrian), Tg (Tajik), Tg (Tigr) , Wiig (Uighur), Urd (Urdu), Uzbek , uzb_cyrl (Uzbek) In addition, Tesseract can be taught to work in other languages. Tesseract can handle right-left text such as Arabic or Hebrew, many indfaacts as well as CJK pretty well. Precision rates are shown in this presentation for tesseract tutorial at DAS 2016, Santorini Ray Smith. Tesseract is suitable for use as a backend and can be used for more complex OCR tasks, including interface layout analysis such as OCRopus. Tesseract output will be of very low quality if the input images are not pre-processed according to it: Images (especially screenshots) should be increased so that the height of the text is at least 20 pixels, any rotation or skew must be corrected or the text is not recognized, low-frequency changes in brightness must be filtered
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