Left = re.findall(r'left:(+)px', div_style) # position:absolute border: textbox 1px solid writing-mode:lr-tb left:292px top:1157px width:27px height:12px With open('example.pdf', 'rb') as pdf_file:Įxtract_text_to_fp(pdf_file, output, laparams=LAParams(), output_type='html', codec=None) Print(time_tabula, time_camelot, time_camelot/time_tabula) Time_camelot = pstats.Stats(prof_camelot).total_tt Prof_camelot = cProfile.Profile().run(cmd_camelot) Time_tabula = pstats.Stats(prof_tabula).total_ttĬmd_camelot = "camelot.read_pdf('table.pdf', pages='1', flavor='lattice')" Prof_tabula = cProfile.Profile().run(cmd_tabula) import camelotĬmd_tabula = "tabula.read_pdf('table.pdf', pages='1', lattice=True)" tables.export('foo.csv', f='csv', compress=False)Įdit: tabula-py appears roughly 6 times faster than camelot-py so that should be used instead. You then can choose how you want to save the tables (as csv, json, excel, html, sqlite), and whether the output should be compressed in a ZIP archive. if you can click and drag to select text in your table in a PDF viewer), then you can use the module camelot-py with import camelot If your pdf is text-based and not a scanned document (i.e. # Sort rows by average height of their center.Ĭenters = Row_cells = sorted( + cells_in_same_row, key=lambda c: c) We’ll remove those rectangles from the list and repeat. Then we’ll sort those rectangles by the x value of their center. Then we’ll find all of the rectangles that have a center that is within the top-y and bottom-y values of that top-left rectangle. We’ll find the rectangle with the most top-left corner. We want to identify the cells from left-to-right, top-to-bottom. The part I will reference will be in sorting the cells. This is very similar to 2, so I won't include all the code. Images = for x, y, w, h in bounding_rects] # Leaving that step as a future TODO if it is ever necessary. # here though which would only have 4 intersections, 1 at each corner. # A table should have a lot of intersections. # additional step to check the number of "joints" inside this bounding rectangle. # The link where a lot of this code was borrowed from recommends an Perimeter_lengths = Įpsilons = Īpprox_polys = īounding_rects = Mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE,Ĭontours = Mask = horizontally_dilated + vertically_dilated Vertically_dilated = cv2.dilate(vertically_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (1, 60))) Horizontally_dilated = cv2.dilate(horizontally_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (40, 1))) Vertically_opened = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, vertical_kernel) Vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, int(image_height / SCALE))) Horizontally_opened = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, horizontal_kernel) Horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (int(image_width / SCALE), 1)) Image_width, image_height = horizontal.shape import cv2īlurred = cv2.GaussianBlur(image, BLUR_KERNEL_SIZE, STD_DEV_X_DIRECTION, STD_DEV_Y_DIRECTION) This link was a good reference while figuring out how to find tables. I'll provide some brief examples for a couple of the steps that do require code. Some of the steps don't require code, they take advantage of external tools like pdfimages and tesseract. I wrote a python package with modules that can help with those steps. Use OpenCV to crop and clean up each cell so that there is no noise that will confuse OCR software.Ĭombine the extracted text of each cell into the format you need. Use OpenCV to find and extract each cell from the table. Use Tesseract to detect rotation and ImageMagick mogrify to fix it. Use pdfimages from to turn the pages of the pdf into images. I could not find a workable off-the-shelf solution nothing that gave me the accuracy I needed. This answer is for anyone encountering pdfs with images and needing to use OCR.
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