我正在尝试将许多PDF文档转换为R中的文本，以便使用字符串解析和正则拨号来从中提取一组代码。我正在使用

Question

我正在尝试从左列中获取代码。我成功提取的唯一代码是描述比单行更长的代码。 我已经使用

magick

进行了各种

PRE加工技术的实验，但在大多数情况下却差不多。我唯一能够获得代码集的实例是从图像中裁剪右侧，但不幸的是，在我的情况下，这不是一个有效的解决方案。

file <- magick::image_read("44F245A2-5FEE-408F-A197-756436A5CAFD.png")

file %>%
  magick::image_resize("2000x") %>%
  magick::image_convert(type = 'Grayscale') %>%
  tesseract::ocr() %>%
  cat()

# or
# descriptions in this document.
# 94942C This is a description that takes on multiple lines. It can contain any combination of
# alphanumeric characters or punctuation. Different types of things can go in here and the
# | terpenes Steet gine see
# 272144 This is a description that takes on multiple lines. It can contain any combination of
# eee
# length of the description could be anywhere from 1 line to 5 lines of text.
# E76744 This is a description that takes on multiple lines. It can contain any combination of
# alphanumeric characters or punctuation. Different types of things can go in here and the
# [terpenes Steet gine see
# K77744 This is a description that takes on multiple lines. It can contain any combination of
# alphanumeric characters or punctuation. Different types of things can go in here and the
# | terrane een Steet gine seem
# 172744 This is a description that takes on multiple lines. It can contain any combination of
# Se
# length of the description could be anywhere from 1 line to 5 lines of text.
# A71744 This is a description that takes on multiple lines. It can contain any combination of
# alphanumeric characters or punctuation. Different types of things can go in here and the
# | teammates Steet gine see

comeyly我希望能够从上面链接中的图像中获取所有代码。任何帮助都很棒。

trory要使用不同的页面细分模式，可用的分割模式为：

Page segmentation modes:
  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    Tre at the image as a single character.
 11    Sparse text. Find as much text as possible in no particular order.
 12    Sparse text with OSD.
 13    Raw line. Treat the image as a single text line,

从我的案例中，从我的经验＃12给出了最多的文字，但可能不是按顺序进行的，但如果您想将代码与描述相关联，这可能是一个问题。