PYPDF2 EXTRACT TEXT EXAMPLE PDF
PYPDF2 EXTRACT TEXT EXAMPLE CODE

PYPDF2 EXTRACT TEXT EXAMPLE CODE

We found several snippets of code that seemed to fit our project perfectly but each time we ran the code we got errors such as NameError: name 'process_pdf' is not defined. We had a couple of false starts with pdfminer, though.

PYPDF2 EXTRACT TEXT EXAMPLE PDF

In the end we chose to use Python вЂ“ converting the PDF to HTML using pdfminer and then using regular expressions to pull out the pieces we needed. There is a command line version of Tabula and it's possible that this is a better option than it seemed and we look forward to learning more about it. In our admittedly very quick experiments with Tabula it appeared that the text areas needed to be manually selected so this didn't seem to be an option for us.

I should note that there is also a new program called Tabula that is geared toward extracting data from PDFs. Another option would be XML but we found that XML output produced too much text. In terms of keeping the useful detail, we found that converting the data to HTML was a good option. Extracting to HTML provided the detail we needed Keeping the formatting detail that you can see in the PDF would be valuable for extracting the data. We quickly found, though, that raw text was not going to give us enough detail or 'signposts' to work with. Extracting the data from these tools produced something that looked like this: 'Changing Echoes 7632 Pool Station Road Angels Camp, CA 95222 (209) 785-3667 Intake: (800) 633-7066 SA s TX DT BU s RS RL s SF PI s AH ANTIOCH Addiction Research and Treatment Inc (ART) 3707 Sunset Lane Antioch, CA 94509 (925) 522-0124 Methadone and Buprenorphine Clients Only SA s TX DT MM DM BU s OP s PW s SF MD PI s SS s SP F4 F28 F30 F81 ' These included the R package tm, the Python pyPDF2 package, PDFtoText/Xpdf, pdf2ascii. We found several good options for converting PDFs to raw text. In the interest of extracting the data programmatically we started with a brief investigation of the various options. Here is an example of what the data looks like: Fortunately, the formatting was reasonably consistent throughout the document вЂ“ phone numbers tended to be in the same format, address elements tended to be in the same order вЂ“ this definitely makes the job easier. The addresses in the PDF were arranged in three columns. For a recent project, however, we were asked to extract detailed address information from a directory (the National Directory of Drug and Alcohol Abuse Treatment Programs) with more than 700 pages, definitely not a job to be done manually. For small PDFs with minimal data or text it's fairly straightforward to extract the data manually by using 'save as' or simply copying and pasting the data you need. It is not uncommon for us to need to extract text from a PDF.

Pypdf2 extract text example