how to extract bold text from a pdf using R

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I have searched through SO and the closest I got to the answer was here. But my requirement is to get a simpler & more elegant way to extract bold from a simple paragraph of text of pdf. The pdftools package only extracts the plain text component. Does anyone know if there is any other way to simply detect bold tokens (or words) from a chunk of text in pdf. I use R so kindly restrict to suggestions in R.

how extract bold text from pdf documents, You need a pdf library such as iTextSharp[^] or commercial library. If you want to go with commericail application, there are many of them to  Question: How to identify bold text in a pdf file using R but not using tabulizer package. Reason: I feel Tabuliser is an overkill if there are no tables in the file. For just plain text paragraphs we need not identify the area of the text.

You don't have to use tabularizer, but I don't know a way that does not involve Java. I had hoped that Apache Tika via the rtika package can be used. However, bold text is not rendered as such. However, one can use pdfbox as shown in that ticket:

 java -jar <pdfbox-jar> ExtractText -html <pdf-file> <html-file>

This command would normally started in a shell, but you can also use system(2) from within R. Then in R use

html <- xml2::read_html(<html-file>)
bold <- xml2::xml_find_all(html, '//b')

to process the HTML file. With your document this returns

{xml_nodeset (6)}
[1] Preamble\n
[2] WHEREAS it is expedient to define and amend certain parts of the law relating to contracts;\n
[3] History\n
[4] Ancient and Medieval Period\n
[5] The Introduction of English Law Into India\n
[6] Mofussal Courts\n

Extracting PDF Text with R and Creating Tidy Data, Yet, sometimes, the data we need is locked away in a file format that is less accessible such as a PDF. If you have ever found yourself in this  Continue reading How to extract data from a PDF file with R In this post, taken from the book R Data Mining by Andrea Cirillo, we'll be looking at how to scrape PDF files using R. It's a relatively straightforward way to look at text mining – but it can be challenging if you don't know exactly what you're doing.

This answer is based on answers received from @hrbmstr and @ralf. So thanks to them. I've made the answers simpler (mainly taking out the peculiarity of the HTML conversion & file naming). Also it is tailored for MAC OS users (perhaps LINUX too) - not sure about Windows guys.

I presume you have pdftohtml installed on your machine. If not use brew install pdftohtml. If you donot have homebrew on your MAC then install it first. A link is provided to help you for homebrew.

Once you are sure pdftohtml is installed on the mac, go with this R function to extract bold from any pdf document.


# pass a pdf file in current directory to this function
extr_bold <- function(file) {
  basefile <- str_remove(file,"\\.pdf|\\.PDF")
  htmlfile <- paste0(basefile,"s",".html")
  if(!exists(htmlfile) ) 
    system2("pdftohtml",args = c("-i",file),stdout=NULL)
  nodevar <- read_html(htmlfile)
  x <- html_nodes(nodevar,xpath = ".//b")

How to extract bold text from HTML or PDF file, PDFs are designed to mimic a printed page, and they are designed only as an output format, not an input format. a PDF is basically a map containing How can I extract text from a two-column image file? Sometimes the text is a graphic r. Yet, sometimes, the data we need is locked away in a file format that is less accessible such as a PDF. If you have ever found yourself in this dilemma, fret not — pdftools has you covered. In this post, you will learn how to: use pdftools to extract text from a PDF, use the stringr package to manipulate strings of text, and create a tidy data set.

how to extract bold text from a pdf using R, how to extract bold text from a pdf using R. Multi tool use. up vote -1 down vote favorite. I have searched through SO and the closest I got to the  Two techniques to extract raw text from PDF files. Use pdftools::pdf_text; Use the tm package; Extract the right information. 1. Clean the headers and footers on all pages. 2. Get the two columns together. 3. Find the rows of the speakers

Pdftools 2.0: powerful pdf text extraction tools, This makes sense for printing, but makes extracting text or data from a pdf file extremely difficult. Because the pdf format has little semantic structure, the pdf_text() We use an example pdf file from the rOpenSci tabulizer package. The tesseract package provides R bindings to the Google Tesseract OCR  I am doing a feature on out product that takes PDF form and annotates to create a new PDF file based on the PDF form, XML file and user selection. I thought writing an article, but we are using thrid party tool to accomplish the task. I am sure I can think of doing it using iTextSharp, but don't have the time right now.

Extracting text fields from a list of pdfs - tidyverse, All files are in a similar format and layout. I have been trying to read all of the .pdf files into R, then extract data from relevant fields for a… We’ll use this vector to automate the process of reading in the text of the PDF files. The pdftools function for extracting text is pdf_text. Using the lapply function, we can apply the pdf_text function to each element in the “files” vector and create an object called “opinions”.

  • "Please don't advice possibly using a particular tool because I am unwilling to do the work necessary to setup a usable data science environment" is not exactly going to cause folks to come running to this question. Thousands of R folks manage to have a working R + rJava environment. It has some headaches. Ultimately it's worth it b/c you get access to a whole world of great Java libraries. Anyway, not going to bother with a full answer but can likely help (but there's that rJava "work" again).
  • Can you provide a sample PDF?
  • Thank you for direct the advice @hrbrmstr. I get what you are trying to say. Apologise for sounding lazy. I will check your link and also try to setup an rJava environment. I guess what I am also understanding, reading in between your lines, is that rJava is essential for a data science environment if I use R. Am I correct?
  • @RalfStubner: I use the pdf on this link here. Although it is a bit large it has all its titles in bold hence is an ideal case of text processing using bold titles as section headers. If you can suggest any easy way (not intending to be lazy but if there's an easier way I would be happy to use it).
  • So your actual aim is not "identify bold text" but "identify section titles "?
  • @hrbmstr : thank you so much. I have started the process of implementation. Seems there is something amiss in the pdftohtml command. After homebrewing poppler (went through successfully on Mac OS Mujave) and creating all the temp directory, copying the pdf file there etc the pdftohtml seems to return a status = 1. Warning message: In system2(command = "pdftohtml", args = args, stdout = TRUE) : running command ''pdftohtml' -i' had status 1; Please note I have not supplied extra_args to pdftohtm. The args = "-i" was only used.
  • Try running it without the function. In a terminal, try just pdftohtml -i thenameofyourpdffile.pdf and then a read_html on the file that has an s towards the end.
  • I managed to convert the pdf to html using the command pdftohtml -i file.pdf followed by bold_tags <- html_nodes(doc, xpath=".//b"); bold_words <- html_text(bold_tags) but the variable bold_words is a 0 length character vector.
  • Can you add what you did and the exact (complete with library calls) R code after that to the original question?
  • Bingo it's working! I was using the incorrect html file out of the three files formed by default. Also, I suggest we simplify the answer for others. I have made this simple function that delivers the output without adding too many checks.
  • I think Ralf your answer uses java hence I am not trying it out of now. Also, the remaining parts seem to be the same as @hrbmstr. So thanks a lot. I am now replicating my working function as a new answer so that others who search on this (including myself) donot get stuck by selecting the incorrect html file.
  • @hrbmstr: could you please check my answer and see if it makes sense? Or does it need some improvement without adding complexity and of course keeping it easy to read. thanks