Package 'tesseract'

Title: Open Source OCR Engine
Description: Bindings to 'Tesseract': a powerful optical character recognition (OCR) engine that supports over 100 languages. The engine is highly configurable in order to tune the detection algorithms and obtain the best possible results.
Authors: Jeroen Ooms [aut, cre]
Maintainer: Jeroen Ooms <[email protected]>
License: Apache License 2.0
Version: 5.2.2
Built: 2024-11-02 05:43:34 UTC
Source: https://github.com/ropensci/tesseract

Help Index


Tesseract OCR

Description

Extract text from an image. Requires that you have training data for the language you are reading. Works best for images with high contrast, little noise and horizontal text. See tesseract wiki and our package vignette for image preprocessing tips.

Usage

ocr(image, engine = tesseract("eng"), HOCR = FALSE)

ocr_data(image, engine = tesseract("eng"))

Arguments

image

file path, url, or raw vector to image (png, tiff, jpeg, etc)

engine

a tesseract engine created with tesseract(). Alternatively a language string which will be passed to tesseract().

HOCR

if TRUE return results as HOCR xml instead of plain text

Details

The ocr() function returns plain text by default, or hOCR text if hOCR is set to TRUE. The ocr_data() function returns a data frame with a confidence rate and bounding box for each word in the text.

References

Tesseract: Improving Quality

See Also

Other tesseract: tesseract(), tesseract_download()

Examples

# Simple example
text <- ocr("https://jeroen.github.io/images/testocr.png")
cat(text)

xml <- ocr("https://jeroen.github.io/images/testocr.png", HOCR = TRUE)
cat(xml)

df <- ocr_data("https://jeroen.github.io/images/testocr.png")
print(df)


# Full roundtrip test: render PDF to image and OCR it back to text
curl::curl_download("https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf", "R-intro.pdf")
orig <- pdftools::pdf_text("R-intro.pdf")[1]

# Render pdf to png image
img_file <- pdftools::pdf_convert("R-intro.pdf", format = 'tiff', pages = 1, dpi = 400)
unlink("R-intro.pdf")

# Extract text from png image
text <- ocr(img_file)
unlink(img_file)
cat(text)


engine <- tesseract(options = list(tessedit_char_whitelist = "0123456789"))

Tesseract Engine

Description

Create an OCR engine for a given language and control parameters. This can be used by the ocr and ocr_data functions to recognize text.

Usage

tesseract(
  language = "eng",
  datapath = NULL,
  configs = NULL,
  options = NULL,
  cache = TRUE
)

tesseract_params(filter = "")

tesseract_info()

Arguments

language

string with language for training data. Usually defaults to eng

datapath

path with the training data for this language. Default uses the system library.

configs

character vector with files, each containing one or more parameter values. These config files can exist in the current directory or one of the standard tesseract config files that live in the tessdata directory. See details.

options

a named list with tesseract parameters. See details.

cache

speed things up by caching engines

filter

only list parameters containing a particular string

Details

Tesseract control parameters can be set either via a named list in the options parameter, or in a config file text file which contains the parameter name followed by a space and then the value, one per line. Use tesseract_params() to list or find parameters. Note that that some parameters are only supported in certain versions of libtesseract, and that invalid parameters can sometimes cause libtesseract to crash.

See Also

Other tesseract: ocr(), tesseract_download()

Examples

tesseract_params('debug')

Tesseract Training Data

Description

Helper function to download training data from the official tessdata repository. On Linux, the fast training data can be installed directly with yum or apt-get.

Usage

tesseract_download(
  lang,
  datapath = NULL,
  model = c("fast", "best"),
  progress = interactive()
)

Arguments

lang

three letter code for language, see tessdata repository.

datapath

destination directory where to download store the file

model

either fast or best is currently supported. The latter downloads more accurate (but slower) trained models for Tesseract 4.0 or higher

progress

print progress while downloading

Details

Tesseract uses training data to perform OCR. Most systems default to English training data. To improve OCR performance for other languages you can to install the training data from your distribution. For example to install the spanish training data:

On Windows and MacOS you can install languages using the tesseract_download function which downloads training data directly from github and stores it in a the path on disk given by the TESSDATA_PREFIX variable.

References

tesseract wiki: training data

See Also

Other tesseract: ocr(), tesseract()

Examples

## Not run: 
if(is.na(match("fra", tesseract_info()$available)))
  tesseract_download("fra", model = 'best')
french <- tesseract("fra")
text <- ocr("https://jeroen.github.io/images/french_text.png", engine = french)
cat(text)

## End(Not run)