R/Python：将 PDF 转换为表格
sta*_*oob 2 python ocr tesseract r data-manipulation
我有一个 PDF 图像文件（例如“p1.pdf” - 这是原始文档的扫描副本）保存在我的计算机上 - 该文件看起来像这样（我添加了红线以显示区别）：
我想将此 PDF 导入到 R 中，并将其转换为“表对象”。我尝试按照本教程（+ https://cran.r-project.org/web/packages/tesseract/vignettes/intro.html ）来做到这一点：
library(tesseract)
library(magick)
library(png)
library(pdftools)
library(tidyverse)
pngfile <- pdftools::pdf_convert('p1.pdf', dpi = 600)
text <- tesseract::ocr(pngfile)
cat(text)
raw_img <- image_read(pngfile)
raw_img %>%
image_ocr()
这似乎有效 - 我继续按照教程进行操作：
num_only <- tesseract::tesseract(
options = list(tessedit_char_whitelist = c(".0123456789 "))
)
raw_img %>%
image_quantize(colorspace = 'gray') %>%
image_threshold() %>%
image_crop(geometry_area(100, 0, 600, 40)) %>%
ocr(engine = num_only)
combo <- tesseract::tesseract(
options = list(
tessedit_char_whitelist = paste0(
c(letters, LETTERS, " ", ".0123456789 (-)"), collapse = "")
))
raw_text <- raw_img %>%
image_quantize(colorspace = "gray") %>%
image_transparent("white", fuzz = 22) %>%
image_background("white") %>%
image_threshold() %>%
image_crop(geometry_area(0, 0, 110, 45)) %>%
ocr(engine = combo)
现在，我尝试将其转换为表格（“tibble”）：
raw_tibble <- raw_text %>%
str_split(pattern = "\n") %>%
unlist() %>%
tibble(data = .)
# A tibble: 68 x 1
data
<chr>
1 "- ALPHABETICAL LISTING ABT"
2 "a PlaceYear of Reg"
3 "Name Address Graduation Year"
4 ""
5 "- (John Smith) BC ABC Uni~
6 ""
7 "Email: jsmith@gmail.com P"999-999-~
8 "BCC University 2002"
9 "- Jane Smith HGH Univer~
10 "Email janes@gmail.com "
# ... with 58 more rows
问题出在此处 - 与教程中进一步整理结果相关的说明似乎非常具体于教程中的示例（即与足球相关）。因此，我无法将它们应用于我的问题。有人可以告诉我如何获取结果并将其转换为看起来更接近原始 PDF 的表格吗？
也许有人可以尝试将我上传的图片保存为 png 文件，看看他们是否可以让这段代码工作？
谢谢！
注1：
- 因此，我只对“名称”和“地址”列感兴趣
- 我也对表格的最终格式有很大的灵活性持开放态度。例如“
401 Fake St Some City, Some State A1C B23 P: 555-555-5555 501 Fake St Some City, Some State A1C B23 p:666-666-6666 601 Fake St Some City, Some State A1C B23 p:666-666-6666”可以全部显示为一行 - 这意味着在最终表中，2组红线之间的所有“条目”都可以用3列和1行表示
因此，最终的表格可能如下所示：
id Name Address
1 1 (John Smith) Email:jsmith@gmail.com AB p:999-999-9999
2 2 Jane Smith Email:janes@gmail.com p:111-111-1111
3 3 Henry Smith 201 Fake St,Some City, Some State A1C B23 P:111-222-3333
4 4 Jason Smith 301 Fake St Some City,Some State A1C B23 P:555-555-5555
5 5 Luke Smith lsmith2@gmail.com 401 Fake St Some City, Some State A1C B23 P: 555-555-5555 501 Fake St Some City, Some State A1C B23 p:666-666-6666 601 Fake St Some City, Some State A1C B23 p:666-666-6666
Place_Year_Graduation Reg_Year
1 ABC University 2001, BCC University 2002,DEF University 2003 2000
2 HGH University/2001, Some Other School 2002 2000
3 University ABC 1999
4 Univer 123 2005
5 ABC College 2010
final = structure(list(id = 1:5, Name = c(" (John Smith) Email:jsmith@gmail.com",
"Jane Smith Email:janes@gmail.com", "Henry Smith", "Jason Smith",
"Luke Smith lsmith2@gmail.com"), Address = c("AB p:999-999-9999",
"p:111-111-1111", "201 Fake St,Some City, Some State A1C B23 P:111-222-3333",
"301 Fake St Some City,Some State A1C B23 P:555-555-5555", "401 Fake St Some City, Some State A1C B23 P: 555-555-5555 501 Fake St Some City, Some State A1C B23 p:666-666-6666 601 Fake St Some City, Some State A1C B23 p:666-666-6666"
), Place_Year_Graduation = c("ABC University 2001, BCC University 2002,DEF University 2003",
"HGH University/2001, Some Other School 2002", "University ABC",
"Univer 123", "ABC College"), Reg_Year = c(2000, 2000, 1999,
2005, 2010)), class = "data.frame", row.names = c(NA, -5L))
注 2：我知道这是一个难题，而且 R 可能不是解决这个问题的最合适工具 - 因此，我也愿意使用 Python 来解决这个问题。