| TESSERACT(1) | TESSERACT(1) |
tesseract - command-line OCR engine
tesseract imagename|listname|stdin outputbase|stdout [options...] [configfile...]
tesseract(1) is a commercial quality OCR engine originally developed at HP between 1985 and 1995. In 1995, this engine was among the top 3 evaluated by UNLV. It was open-sourced by HP and UNLV in 2005, and has been developed at Google since then.
imagename
listname
stdin
outputbase
stdout
--tessdata-dir /path
--user-words /path/to/file
--user-patterns /path/to/file
-c configvar=value
-l lang
--psm N
0 = Orientation and script detection (OSD) only. 1 = Automatic page segmentation with OSD. 2 = Automatic page segmentation, but no OSD, or OCR. 3 = Fully automatic page segmentation, but no OSD. (Default) 4 = Assume a single column of text of variable sizes. 5 = Assume a single uniform block of vertically aligned text. 6 = Assume a single uniform block of text. 7 = Treat the image as a single text line. 8 = Treat the image as a single word. 9 = Treat the image as a single word in a circle. 10 = Treat the image as a single character.
--oem N
0 = Original Tesseract only. 1 = Neural nets LSTM only. 2 = Tesseract + LSTM. 3 = Default, based on what is available.
configfile
It is possible to select several config files, for example tesseract image.png demo hocr pdf txt will create three output files demo.hocr, demo.pdf and demo.txt with the OCR results.
Nota Bene: The options -l lang and --psm N must occur before any configfile.
-h, --help
--help-psm
--help-oem
-v, --version
--list-langs
--print-parameters
To recognize some text with Tesseract, it is normally necessary to specify the language(s) or script of the text (unless it is English text which is supported by default) using -l lang.
Selecting a language automatically also selects the language specific character set and dictionary (word list).
Selecting a script typically selects all characters of that script which can be from different languages. The dictionary which is included also contains a mix from different languages. In most cases, a script also supports English. So it is possible to recognize a language that has not been specifically trained for by using traineddata for the script it is written in.
https://github.com/tesseract-ocr/tessdata_fast provides fast language and script models which are also part of Linux distributions.
For Tesseract 4, tessdata_fast includes traineddata files for the following languages:
afr (Afrikaans), amh (Amharic), ara (Arabic), asm (Assamese), aze (Azerbaijani), aze_cyrl (Azerbaijani - Cyrilic), bel (Belarusian), ben (Bengali), bod (Tibetan), bos (Bosnian), bre (Breton), bul (Bulgarian), cat (Catalan; Valencian), ceb (Cebuano), ces (Czech), chi_sim (Chinese - Simplified), chi_tra (Chinese - Traditional), chr (Cherokee), cym (Welsh), dan (Danish), deu (German), dzo (Dzongkha), ell (Greek, Modern (1453-)), eng (English), enm (English, Middle (1100-1500)), epo (Esperanto), equ (Math / equation detection module), est (Estonian), eus (Basque), fas (Persian), fin (Finnish), fra (French), frk (Frankish), frm (French, Middle (ca.1400-1600)), gle (Irish), glg (Galician), grc (Greek, Ancient (to 1453)), guj (Gujarati), hat (Haitian; Haitian Creole), heb (Hebrew), hin (Hindi), hrv (Croatian), hun (Hungarian), iku (Inuktitut), ind (Indonesian), isl (Icelandic), ita (Italian), ita_old (Italian - Old), jav (Javanese), jpn (Japanese), kan (Kannada), kat (Georgian), kat_old (Georgian - Old), kaz (Kazakh), khm (Central Khmer), kir (Kirghiz; Kyrgyz), kor (Korean), kor_vert (Korean (vertical)), kur (Kurdish), kur_ara (Kurdish (Arabic)), lao (Lao), lat (Latin), lav (Latvian), lit (Lithuanian), ltz (Luxembourgish), mal (Malayalam), mar (Marathi), mkd (Macedonian), mlt (Maltese), mon (Mongolian), mri (Maori), msa (Malay), mya (Burmese), nep (Nepali), nld (Dutch; Flemish), nor (Norwegian), oci (Occitan (post 1500)), ori (Oriya), osd (Orientation and script detection module), pan (Panjabi; Punjabi), pol (Polish), por (Portuguese), pus (Pushto; Pashto), que (Quechua), ron (Romanian; Moldavian; Moldovan), rus (Russian), san (Sanskrit), sin (Sinhala; Sinhalese), slk (Slovak), slv (Slovenian), snd (Sindhi), spa (Spanish; Castilian), spa_old (Spanish; Castilian - Old), sqi (Albanian), srp (Serbian), srp_latn (Serbian - Latin), sun (Sundanese), swa (Swahili), swe (Swedish), syr (Syriac), tam (Tamil), tat (Tatar), tel (Telugu), tgk (Tajik), tgl (Tagalog), tha (Thai), tir (Tigrinya), ton (Tonga), tur (Turkish), uig (Uighur; Uyghur), ukr (Ukrainian), urd (Urdu), uzb (Uzbek), uzb_cyrl (Uzbek - Cyrilic), vie (Vietnamese), yid (Yiddish), yor (Yoruba)
To use a non-standard language pack named foo.traineddata, set the TESSDATA_PREFIX environment variable so the file can be found at TESSDATA_PREFIX/tessdata/foo.traineddata and give Tesseract the argument -l foo.
For Tesseract 4, tessdata_fast includes traineddata files for the following scripts:
Arabic, Armenian, Bengali, Canadian Aboriginal, Cherokee, Cyrillic, Devanagari, Ethiopic, Fraktur, Georgian, Greek, Gujarati, Gurmukhi, Han - Simplified, Han - Simplified (vertical), Han - Traditional, Han - Traditional (vertical), Hangul, Hangul (vertical), Hebrew, Japanese, Japanese (vertical), Kannada, Khmer, Lao, Latin, Malayalam, Myanmar, Oriya (Odia), Sinhala, Syriac, Tamil, Telugu, Thaana, Thai, Tibetan, Vietnamese.
The same languages and scripts are available from https://github.com/tesseract-ocr/tessdata_best. tessdata_best provides slow language and script models. These models are needed for training. They also can give better OCR results, but the recognition takes much more time.
Both tessdata_fast and tessdata_best only support the LSTM OCR engine.
There is a third repository, https://github.com/tesseract-ocr/tessdata, with models which support both the Tesseract 3 legacy OCR engine and the Tesseract 4 LSTM OCR engine.
Tesseract config files consist of lines with variable-value pairs (space separated). The variables are documented as flags in the source code like the following one in tesseractclass.h:
STRING_VAR_H(tessedit_char_blacklist, "", "Blacklist of chars not to recognize");
These variables may enable or disable various features of the engine, and may cause it to load (or not load) various data. For instance, let’s suppose you want to OCR in English, but suppress the normal dictionary and load an alternative word list and an alternative list of patterns — these two files are the most commonly used extra data files.
If your language pack is in /path/to/eng.traineddata and the hocr config is in /path/to/configs/hocr then create three new files:
/path/to/eng.user-words:
the quick brown fox jumped
/path/to/eng.user-patterns:
1-\d\d\d-GOOG-411 www.\n\\\*.com
/path/to/configs/bazaar:
load_system_dawg F load_freq_dawg F user_words_suffix user-words user_patterns_suffix user-patterns
Now, if you pass the word bazaar as a trailing command line parameter to Tesseract, Tesseract will not bother loading the system dictionary nor the dictionary of frequent words and will load and use the eng.user-words and eng.user-patterns files you provided. The former is a simple word list, one per line. The format of the latter is documented in dict/trie.h on read_pattern_list().
The engine was developed at Hewlett Packard Laboratories Bristol and at Hewlett Packard Co, Greeley Colorado between 1985 and 1994, with some more changes made in 1996 to port to Windows, and some C++izing in 1998. A lot of the code was written in C, and then some more was written in C++. The C++ code makes heavy use of a list system using macros. This predates stl, was portable before stl, and is more efficient than stl lists, but has the big negative that if you do get a segmentation violation, it is hard to debug.
Version 2.00 brought Unicode (UTF-8) support, six languages, and the ability to train Tesseract.
Tesseract was included in UNLV’s Fourth Annual Test of OCR Accuracy. See https://github.com/tesseract-ocr/docs/blob/master/AT-1995.pdf. With Tesseract 2.00, scripts are now included to allow anyone to reproduce some of these tests. See https://github.com/tesseract-ocr/tesseract/wiki/TestingTesseract for more details.
Tesseract 3.00 added a number of new languages, including Chinese, Japanese, and Korean. It also introduced a new, single-file based system of managing language data.
Tesseract 3.02 added BiDirectional text support, the ability to recognize multiple languages in a single image, and improved layout analysis.
Tesseract 4 adds a new neural net (LSTM) based OCR engine which is focused on line recognition, but also still supports the legacy Tesseract OCR engine of Tesseract 3 which works by recognizing character patterns. Compatibility with Tesseract 3 is enabled by --oem 0. This also needs traineddata files which support the legacy engine, for example those from the tessdata repository (https://github.com/tesseract-ocr/tessdata).
For further details, see the release notes in the Tesseract wiki (https://github.com/tesseract-ocr/tesseract/wiki/ReleaseNotes).
Main web site: https://github.com/tesseract-ocr User forum: http://groups.google.com/group/tesseract-ocr Wiki: https://github.com/tesseract-ocr/tesseract/wiki Information on training: https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract
ambiguous_words(1), cntraining(1), combine_tessdata(1), dawg2wordlist(1), shape_training(1), mftraining(1), unicharambigs(5), unicharset(5), unicharset_extractor(1), wordlist2dawg(1)
Tesseract development was led at Hewlett-Packard and Google by Ray Smith. The development team has included:
Ahmad Abdulkader, Chris Newton, Dan Johnson, Dar-Shyang Lee, David Eger, Eric Wiseblatt, Faisal Shafait, Hiroshi Takenaka, Joe Liu, Joern Wanke, Mark Seaman, Mickey Namiki, Nicholas Beato, Oded Fuhrmann, Phil Cheatle, Pingping Xiu, Pong Eksombatchai (Chantat), Ranjith Unnikrishnan, Raquel Romano, Ray Smith, Rika Antonova, Robert Moss, Samuel Charron, Sheelagh Lloyd, Shobhit Saxena, and Thomas Kielbus.
For a list of contributors see https://github.com/tesseract-ocr/tesseract/blob/master/AUTHORS.
Licensed under the Apache License, Version 2.0
| 01/21/2019 |