Using the Tesseract OCR engine in R

2025-03-23

Extract Text from Images

OCR is the process of finding and recognizing text inside images, forexample from a screenshot, scanned paper. The image below has someexample text:

library(tesseract)eng <- tesseract("eng")text <- tesseract::ocr("http://jeroen.github.io/images/testocr.png", engine = eng)cat(text)

## This is a lot of 12 point text to test the## ocr code and see if it works on all types## of file format.## ## The quick brown dog jumped over the## lazy fox. The quick brown dog jumped## over the lazy fox. The quick brown dog## jumped over the lazy fox. The quick## brown dog jumped over the lazy fox.

Not bad! Theocr_data() function returns all words inthe image along with a bounding box and confidence rate.

results <- tesseract::ocr_data("http://jeroen.github.io/images/testocr.png", engine = eng)results

## # A tibble: 60 × 3##    word  confidence bbox          ##    <chr>      <dbl> <chr>         ##  1 This        96.8 36,92,96,116  ##  2 is          96.9 109,92,129,116##  3 a           95.7 141,98,156,116##  4 lot         95.7 169,92,201,116##  5 of          96.5 212,92,240,116##  6 12          96.5 251,92,282,116##  7 point       96.4 296,92,364,122##  8 text        96.2 374,93,427,116##  9 to          96.9 437,93,463,116## 10 test        97.0 474,93,526,116## # ℹ 50 more rows

Language Data

The tesseract OCR engine uses language-specific training data in therecognize words. The OCR algorithms bias towards words and sentencesthat frequently appear together in a given language, just like the humanbrain does. Therefore the most accurate results will be obtained whenusing training data in the correct language.

Usetesseract_info() to list the languages that youcurrently have installed.

tesseract_info()

## $datapath## [1] "/Users/jeroen/Library/Application Support/tesseract5/tessdata/"## ## $available## [1] "eng"  "nld"  "osd"  "snum"## ## $version## [1] "5.5.0"## ## $configs##  [1] "alto"             "ambigs.train"     "api_config"       "bigram"          ##  [5] "box.train"        "box.train.stderr" "digits"           "get.images"      ##  [9] "hocr"             "inter"            "kannada"          "linebox"         ## [13] "logfile"          "lstm.train"       "lstmbox"          "lstmdebug"       ## [17] "makebox"          "pdf"              "quiet"            "rebox"           ## [21] "strokewidth"      "tsv"              "txt"              "unlv"            ## [25] "wordstrbox"

By default the R package only includes English training data. Windowsand Mac users can install additional training data usingtesseract_download(). Let’s OCR a screenshot from Wikipediain Dutch (Nederlands)

# Only need to do download once:tesseract_download("nld")

# Now load the dictionary(dutch <- tesseract("nld"))

## <tesseract engine>##  loaded: nld ##  datapath: /Users/jeroen/Library/Application Support/tesseract5/tessdata/ ##  available: eng nld osd snum

text <- ocr("https://jeroen.github.io/images/utrecht2.png", engine = dutch)cat(text)

## Geschiedenis van de stad Utrecht## ## In de geschiedenis van de stad Utrecht vond reeds in de prehistorie## kleinschalige bewoning plaats. De Romeinen bouwden rond 50 n.Chr. in het## kader van een zeer omvangrijk militair bouwproject langs de toenmalige## Rijnloop in Utrecht het fort Traiectum ter hoogte van het Domplein. Hierdoor## werd de grondslag voor de stad gelegd. Na het vertrek van de Romeinen## rond 270, vestigden in het midden van de 5e eeuw Franken zich in de regio.## Vanaf de 7e eeuw tot het begin van de 8e eeuw zou dat tot conflicten met## Friezen leiden.## ## Rond het jaar 700 arriveerden Angelsaksische missionarissen om het## gebied te kerstenen en vestigden in het oude Romeinse fort daarvoor hun## basis onder Frankische bescherming. Het groeide uit tot de burcht Trecht en## het kerkelijk centrum. Hiernaast ontstond in de 10e eeuw met Stathe een## bloeiend handelscentrum met koop- en ambachtslieden.## ## In 1122 verkreeg Utrecht stadsrechten en kort daarop werden stadswallen## met een verdedigingsgracht om de stad aangelegd. Door verdere groei was## Utrecht tot halverwege de 16e eeuw de grootste stad van de Noordelijke## Nederlanden.

As you can see immediately: almost perfect! (OK just take myword).

Preprocessing with Magick

The accuracy of the OCR process depends on the quality of the inputimage. You can often improve results by properly scaling the image,removing noise and artifacts or cropping the area where the text exists.Seetesseractwiki: improve quality for important tips to improve the quality ofyour input image.

The awesomemagickR package has many useful functions that can be use for enhancing thequality of the image. Some things to try:

• If your image is skewed, useimage_deskew() andimage_rotate() make the text horizontal.
• image_trim() crops out whitespace in the margins.Increase thefuzz parameter to make it work for noisywhitespace.
• Useimage_convert() to turn the image into greyscale,which can reduce artifacts and enhance actual text.
• If your image is very large or small resizing withimage_resize() can help tesseract determine text size.
• Useimage_modulate() orimage_contrast()orimage_contrast() to tweak brightness / contrast if thisis an issue.
• Tryimage_reducenoise() for automated noise removal.Your mileage may vary.
• Withimage_quantize() you can reduce the number ofcolors in the image. This can sometimes help with increasing contrastand reducing artifacts.
• True imaging ninjas can useimage_convolve() to usecustomconvolutionmethods.

Below is an example OCR scan. The code converts it to black-and-whiteand resizes + crops the image before feeding it to tesseract to get moreaccurate OCR results.

library(magick)

## Linking to ImageMagick 6.9.12.93## Enabled features: cairo, fontconfig, freetype, heic, lcms, pango, raw, rsvg, webp## Disabled features: fftw, ghostscript, x11

input <- image_read("https://jeroen.github.io/images/bowers.jpg")text <- input %>%  image_resize("2000x") %>%  image_convert(type = 'Grayscale') %>%  image_trim(fuzz = 40) %>%  image_write(format = 'png', density = '300x300') %>%  tesseract::ocr() cat(text)

## The Life and Work of## Fredson Bowers## by## G. THOMAS TANSELLE## ## N EVERY FIELD OF ENDEAVOR THERE ARE A FEW FIGURES WHOSE ACCOM-## plishment and influence cause them to be the symbols of their age;## their careers and oeuvres become the touchstones by which the## field is measured and its history told. In the related pursuits of## analytical and descriptive bibliography, textual criticism, and scholarly## editing, Fredson Bowers was such a figure, dominating the four decades## after 1949, when his Principles of Bibliographical Description was pub-## lished. By 1973 the period was already being called “the age of Bowers”:## in that year Norman Sanders, writing the chapter on textual scholarship## for Stanley Wells's Shakespeare: Select Bibliographies, gave this title to## a section of his essay. For most people, it would be achievement enough## to rise to such a position in a field as complex as Shakespearean textual## studies; but Bowers played an equally important role in other areas.## Editors of nineteenth-century American authors, for example, would## also have to call the recent past “the age of Bowers,” as would the writers## of descriptive bibliographies of authors and presses. His ubiquity in## the broad field of bibliographical and textual study, his seemingly com-## plete possession of it, distinguished him from his illustrious predeces-## sors and made him the personification of bibliographical scholarship in## ## his time.## ## When in 1969 Bowers was awarded the Gold Medal of the Biblio-## graphical Society in London, John Carter’s citation referred to the## Principles as “majestic,” called Bowers’s current projects “formidable,”## said that he had “imposed critical discipline” on the texts of several## authors, described Studies in Bibliography as a “great and continuing## achievement,” and included among his characteristics “uncompromising## seriousness of purpose” and “professional intensity.” Bowers was not## unaccustomed to such encomia, but he had also experienced his share of## attacks: his scholarly positions were not universally popular, and he## expressed them with an aggressiveness that almost seemed calculated to

Read from PDF files

If your images are stored in PDF files they first need to beconverted to a proper image format. We can do this in R using thepdf_convert function from the pdftools package. Use a highDPI to keep quality of the image.

pngfile <- pdftools::pdf_convert('https://jeroen.github.io/images/ocrscan.pdf', dpi = 600)

## Converting page 1 to ocrscan_1.png... done!

text <- tesseract::ocr(pngfile)cat(text)

## | SAPORS LANE - BOOLE - DORSET - BH25 8 ER## TELEPHONE BOOLE (94513) 51617 - TELEX 123456## ## Our Ref. 350/PJC/EAC 18th January, 1972.## Dr. P.N. Cundall,## Mining Surveys Ltd.,## Holroyd Road,## Reading,## Berks.## Dear Pete,## ## Permit me to introduce you to the facility of facsimile## transmission.## ## In facsimile a photocell is caused to perform a raster scan over## ## the subject copy. The variations of print density on the document## cause the photocell to generate an analogous electrical video signal.## This signal is used to modulate a carrier, which is transmitted to a## remote destination over a radio or cable communications link.## ## At the remote terminal, demodulation reconstructs the video## signal, which is used to modulate the density of print produced by a## printing device. This device is scanning in a raster scan synchronised## with that at the transmitting terminal. As a result, a facsimile## copy of the subject document is produced.## ## Probably you have uses for this facility in your organisation.## ## Yours sincerely,## P.J. CROSS## Group Leader - Facsimile Research## Registered in England: No. 2038## No. 1 Registered Office: GO Vicara Lane, Ilford. Essex.

Tesseract Control Parameters

Tesseract supports hundreds of “control parameters” which alter theOCR engine. Usetesseract_params() to list all parameterswith their default value and a brief description. It also has a handyfilter argument to quickly find parameters that match aparticular string.

# List all parameters with *colour* in name or descriptiontesseract_params('colour')

## # A tibble: 2 × 3##   param                      default desc                    ## * <chr>                      <chr>   <chr>                   ## 1 editor_image_word_bb_color 7       Word bounding box colour## 2 editor_image_blob_bb_color 4       Blob bounding box colour

Do note that some of the control parameters have changed betweenTesseract engine 3 and 4.

tesseract::tesseract_info()['version']

## $version## [1] "5.5.0"

numbers <- tesseract(options = list(tessedit_char_whitelist = "$.0123456789"))cat(ocr("https://jeroen.github.io/images/receipt.png", engine = numbers))

## $90.52## $81.52## $9.00## $90.52

# Do not allow any dollar sign numbers2 <- tesseract(options = list(tessedit_char_whitelist = ".0123456789"))cat(ocr("https://jeroen.github.io/images/receipt.png", engine = numbers2))

## 90.52## 81.52## 9.00## 90.52