Once they are accurate, move them to a new subdirectory of the tesstrain repo Since the first few runs will be untrained, you’ll probably gt.txt files contain an accurate recognition of theĬorresponding image. Run the ocr_tables script on a few pdfs to generate some training data. It’s a very good idea to train tesseract. Here is an the an example of the result of the find_tables function. images = for x, y, w, h in bounding_rects] # Leaving that step as a future TODO if it is ever necessary. We might have a rectangular image # here though which would only have 4 intersections, 1 at each corner. # A table should have a lot of intersections.

# The link where a lot of this code was borrowed from recommends an # additional step to check the number of 'joints' inside this bounding rectangle. 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)) SCALE = 5 image_width, image_height = horizontal.shape MAX_COLOR_VAL = 255 BLOCK_SIZE = 15 SUBTRACT_FROM_MEAN = - 2 img_bin = cv2.adaptiveThreshold( STD_DEV_X_DIRECTION = 0 STD_DEV_Y_DIRECTION = 0 blurred = cv2.GaussianBlur(image, BLUR_KERNEL_SIZE, STD_DEV_X_DIRECTION, STD_DEV_Y_DIRECTION) format(re.escape(file_prefix)), filename) Image_filenames = find_matching_files_in_dir(filename_sans_ext, directory) ''' directory, filename = os.path.split(pdf_filepath)įilename_sans_ext = filename.split( '.pdf') This should work up to pdfs with 999 pages since find matching files in dir uses 3 digits in its regex. Images are prefixed by their name sans extension and suffixed by their page number. ''' Uses the `pdfimages` utility from Poppler (). # Since pdfimages creates a number of files named each for there page number # and doesn't return us the list that it created return

Image_filenames = pdfimages(pdf_filepath) ''' Turn a pdf into images ''' directory, filename = os.path.split(pdf_filepath) # Wrapper around the Poppler command line utility 'pdfimages' and helpers for # finding the output files of that command.

Ocr pdf to excel python