ocr | R Documentation |
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.
ocr(image, engine = tesseract("eng"), HOCR = FALSE)
ocr_data(image, engine = tesseract("eng"))
image |
file path, url, or raw vector to image (png, tiff, jpeg, etc) |
engine |
a tesseract engine created with |
HOCR |
if |
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.
Other tesseract:
tesseract()
,
tesseract_download()
# 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"))
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