NB : Since my input is pretty complex with many different tables I ended up using both Camelot and Tabula, depending on the table, to achieve the best results.Photo by James Harrison on Unsplash Introductionīeing a high-level, interpreted language with a relatively easy syntax, Python is perfect even for those who don’t have prior programming experience. It can also output results as CSV, JSON, HTML or Excel.Ĭamelot comes at the expense of a number of dependencies. (Not to be confused with the camelot package.) import camelot Furthermore it has its own accuracy indicator ( results.parsing_report), and great debugging features.īoth Camelot and Tabula provide the results as Pandas’ DataFrames, so it is easy to adjust tables afterwards. Print("Stopped Reading Page: ", i 1, "\n -=-")Ĭamelot seems a fairly powerful solution to extract tables from PDFs in Python.Īt first sight it seems to achieve almost as accurate extraction as the tabula-py package suggested by CreekGeek, which is already waaaaay above any other posted solution as of today in terms of reliability, but it is supposedly much more configurable. Print("\nStarting to Read Page: ", i 1, "\n -=-") Print("\nPrinting Table Content: \n", df)ĭef tiff_header_for_CCITT(self, width, height, img_size, CCITT_group=4):įile = str(i 1) "_" downloaded_file Interpreter = PDFPageInterpreter(pdfResourceManager, device)įor page in PDFPage.get_pages(fp, page_num, maxpages=max_pages, password=password, caching=caching, PdfResourceManager = PDFResourceManager()ĭevice = TextConverter(pdfResourceManager, retstr, codec='utf-8', laparams=la_params) Pdf_reader = PdfFileReader(open(file, 'rb')) With open(str(i 1) "_" filename, "wb") as outputStream: Pdf_reader = PdfFileReader(open(filename, "rb")) Local_filename = local_filename.replace(" ", "_")ĭef break_pdf(self, filename, start_page=-1, end_page=-1): It is working fine for me: # This works in python 3įrom PyPDF2 import PdfFileWriter, PdfFileReader Interpreter = PDFPageInterpreter(rsrcmgr, device) With TextConverter(rsrcmgr, retstr, codec=codec, #Python pdf extract text pdf#'''Convert pdf content from a file path to text #Python pdf extract text install#Test pdf file: #pip install pdfminer.sixįrom pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreterįrom nverter import TextConverter In 2020 the solutions above were not working for the particular pdf I was working with. As instructions for this would blow up this answer I put them on my personal blog. There is pdftotext which does basically the same but this assumes pdftotext in /usr/local/bin whereas I am using this in AWS lambda and wanted to use it from the current directory.ītw: For using this on lambda you need to put the binary and the dependency to libstdc .so into your lambda function. Res = n(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE) SCRIPT_DIR = os.path.dirname(os.path.abspath(_file_)) pikepdf does not support text extraction ( source)Īfter trying textract (which seemed to have too many dependencies) and pypdf2 (which could not extract text from the pdfs I tested with) and tika (which was too slow) I ended up using pdftotext from xpdf (as already suggested in another answer) and just called the binary from python directly (you may need to adapt the path to pdftotext): import os, subprocess.Pymupdf import fitz # install using: pip install PyMuPDF Please note that those packages are not maintained: Give it a try :-) from PyPDF2 import PdfReader And some might have too restrictive licenses so that you may not use it.Įdit: I recently became the maintainer of PyPDF2! ? The community improved the text extraction a lot. But they are not pure-Python which can mean that you cannot execute it. The core part is that they are way faster. Pymupdf / tika / PDFium are better than PyPDF2, but the difference became rather small. Depending on the data, it is on-par or better than pdfminer.six.
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