I'm extracting this PDF's text using the PyPDF2 Python package (version 1.27.2): import PyPDF2 with open("sample.pdf", "rb") as pdf_file: read_pdf = PyPDF2.PdfFileReader(pd... More on stackoverflow.com

Hi everyone, I need some advices. Some people recommend me to use python wrappers (poppler pdfto text) to extract data from this PDF file, from page 4 to end or known limit (here: page 605). But I never used poppler pdfto text before and need some help, please. More on discuss.python.org

I have encountered some similar challenges as you have and have solved them by utilizing PDFplumber. I have parsed through a document of 100k pages with no issues. Here's how I did it. I see that you are using regex module which suggests to me that you have to iterate the text line by line. text_lines = page.extract_text() for line in text_lines.split("\n"): if re.match(line): ... extract_text method unfortunately leaks memory and that's the reason why you get an error with large documents (see Github for discussion). However, I noticed that the objects generated by extract_words are correctly being garbage collected allowing you to process large documents without any problems. Using extract_words has the added benefit that you can often get rid of regular expressions. Extract_words allow you to pin point specific parts of the document using the x0,y0,x1,y0 and top coordinates. Note: this code is untested and solely for illustrative purposes. from operator import itemgetter import pdfplumber # page.crop if needed bounding_box = (0, 700, 100, 700) page = page.crop(bounding_box) settings = {"x_tolerance" : 2, "y_tolerance": 2} words = page.extract_words(settings) # a list of list of dictionaries that represents "new lines", essentially simulating the above example. Contains more information, e.g. x0,y0,x1,y1 coordinates # split document into lines that differ by 1.6 from each other based on top coordinate lines_by_top_coordinate = pdfplumber.utils.cluster_objects(words, itemgetter("top"), 1.6) # iterate line by line for idx, line in enumerate(lines_by_top_coordinate): # build the text_line for possible regex if needed text_line = " ".join(w["text"] for w in line) # allows regexes if re.match(pat, text_line): ... # or get the text based on coordinates, no regexes needed text_line = " ".join(w["text"] for w in line if w["x0"] > 400 and w["x1"] < 500) # or if the document has information that is located # below an indicator, e.g a header text_line = " ".join(w["text"] for lines_by_top_coordinate[idx + 1] in line) Guaranteed this method is not the fastest, but allows you to be more precise with the data extraction and solves the large document problem you are facing. Hope this helps! Let me know if you need further assistance. Edit. typos and fixes More on reddit.com

If you’re ever building something more production-level or need deeper control (like merging, cropping, rotating, or handling PDFs and other formats across platforms). Take a look at Apryse. It’s not open source, but their Python SDK is super robust and covers everything from text extraction to page manipulation. More on reddit.com