@ -26,16 +26,33 @@ output~ to a code block will minimize that noise.
#+BEGIN_SRC shell :results none :session *Shell*
TABLES=("/tmp/example-1/example-1.pdf" "/tmp/example-2/example-2.pdf")
python -m table_ocr.prepare_pdfs $TABLES | grep .png > /tmp/pdf_images.txt
# All pngs that don't have "table" in their name. Assume "table" has already been found for files with table in name.
cat /tmp/pdf-images.txt | xargs -I{} python -m table_ocr.extract_tables {} # | grep tables > /tmp/extracted-tables.txt
cat /tmp/extracted-tables.txt | xargs -I{} python -m table_ocr.extract_cells_from_table {} # | grep cells > /tmp/extracted-cells.txt
python -m table_ocr.prepare_pdfs $TABLES | grep .png > /tmp/pdf-images.txt
cat /tmp/pdf-images.txt | xargs -I{} python -m table_ocr.extract_tables {} | grep table > /tmp/extracted-tables.txt
cat /tmp/extracted-tables.txt | xargs -I{} python -m table_ocr.extract_cells_from_table {} | grep cells > /tmp/extracted-cells.txt
cat /tmp/extracted-cells.txt | xargs -I{} python -m table_ocr.ocr_image {}
# This next one needs to be run on each subdirectory one at a time.
python -m table_ocr.ocr_to_csv $(find . -iregex ".*cells.*ocr_data.* \.txt" 2>/dev/null)
#+END_SRC
Or, as a shell script.
#+BEGIN_SRC shell :results none :tangle ocr_tables :tangle-mode (identity #o755)
#!/bin/sh
PDF=$1
python -m table_ocr.prepare_pdfs $PDF | grep .png > /tmp/pdf-images.txt
cat /tmp/pdf-images.txt | xargs -I{} python -m table_ocr.extract_tables {} | grep table > /tmp/extracted-tables.txt
cat /tmp/extracted-tables.txt | xargs -I{} python -m table_ocr.extract_cells_from_table {} | grep cells > /tmp/extracted-cells.txt
cat /tmp/extracted-cells.txt | xargs -I{} python -m table_ocr.ocr_image {} --psm 7 -l data-table
for image in $(cat /tmp/extracted-tables.txt); do
dir=$(dirname $image)
python -m table_ocr.ocr_to_csv $(find $dir/cells -name "*.txt")
done
#+END_SRC
* Preparing data
** Converting PDFs to images
@ -506,30 +523,32 @@ def crop_to_text(image):
SUBTRACT_FROM_MEAN,
)
# Get rid of littl noise.
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3 ))
opened = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel )
# Dilate so each digit is connected, so we can get a bounding rectangle
# around all of the digits as one contour. This will make the bounding
# rectangle 8 pixels wider on the left and right, so we'll need to crop that
# out at the end so that we don't pick up stray border pixels.
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (16, 1))
dilated = cv2.dilate(opened, kernel )
contours, hierarchy = cv2.findContours(dilated, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
img_h, img_w = image.shape
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (int(img_w * 0.5), 1 ))
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, int(img_h * 0.7)) )
horizontal_lines = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, horizontal_kernel)
vertical_lines = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, vertical_kernel)
both = horizontal_lines + vertical_lines
cleaned = img_bin - both
# Get rid of little noise.
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3) )
opened = cv2.morphologyEx(cleaned, cv2.MORPH_OPEN, kernel)
contours, hierarchy = cv2.findContours(opened, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
bounding_rects = [cv2.boundingRect(c) for c in contours]
NUM_PX_COMMA = 6
if bounding_rects:
# The largest contour is certainly the text that we're looking for.
largest_rect = max(bounding_rects, key=lambda r: r[2] * r[3])
x, y, w, h = largest_rect
# Commas sometimes go a little below the bounding box and we don't want
# to lost them or turn them into periods.
img_h, img_w = image.shape
cropped = image[y:min(img_h, y+h+6), x+8:x+w-8]
minx, miny, maxx, maxy = math.inf, math.inf, 0, 0
for x, y, w, h in bounding_rects:
minx = min(minx, x)
miny = min(miny, y)
maxx = max(maxx, x + w)
maxy = max(maxy, y + h)
x, y, w, h = minx, miny, maxx - minx, maxy - miny
cropped = image[y:min(img_h, y+h+NUM_PX_COMMA), x:min(img_w, x+w)]
else:
# If we morphed out all of the text, fallback to using the unmorphed image.
cropped = image
bordered = cv2.copyMakeBorder(cropped, 5, 5, 5, 5, cv2.BORDER_CONSTANT, None, 255)
return bordered
@ -549,7 +568,6 @@ cv2.imwrite("resources/examples/example-table-cell-1-1-cropped.png", image)
#+ATTR_HTML : :width 200px :height 100%
[[file:resources/examples/example-table-cell-1-1-cropped.png ]]
** OCR each cell
If we cleaned up the images well enough, we might get some accurate OCR!
@ -813,31 +831,44 @@ python -m table_ocr.ocr_cell resources/examples/cells/000-000.png
: PRIZE
#+BEGIN_SRC python :tangle table_ocr/ocr_image.py :mkdirp yes :results none
import argparse
import math
import os
import sys
import cv2
import pytesseract
description="""Takes a single argument that is the image to OCR.
Remaining arguments are passed directly to Tesseract.
Attempts to make OCR more accurate by performing some modifications on the image.
Saves the modified image and the OCR text in an `ocr_data` directory.
Filenames are of the format for training with tesstrain."""
parser = argparse.ArgumentParser(description=description)
parser.add_argument("image", help="filepath of image to perform OCR")
<<crop-to-text >>
<<ocr-image >>
def main(f):
directory, filename = os.path.split(f)
def main(image_ file, tess_args ):
directory, filename = os.path.split(image_ file )
filename_sans_ext, ext = os.path.splitext(filename)
image = cv2.imread(f, cv2.IMREAD_GRAYSCALE)
image = cv2.imread(image_ file , cv2.IMREAD_GRAYSCALE)
cropped = crop_to_text(image)
ocr_data_dir = os.path.join(directory, "ocr_data")
os.makedirs(ocr_data_dir, exist_ok=True)
out_imagepath = os.path.join(ocr_data_dir, filename)
out_txtpath = os.path.join(ocr_data_dir, "{}.gt.txt".format(filename_sans_ext))
cv2.imwrite(out_imagepath, cropped)
txt = ocr_image(cropped, "--psm 7")
txt = ocr_image(cropped, " ".join(tess_args))
print(txt)
with open(out_txtpath, "w") as txt_file:
txt_file.write(txt)
if __name__ == "__main__":
main(sys.argv[1])
args, tess_args = parser.parse_known_args()
main(args.image, tess_args)
#+END_SRC
*** table_ocr/ocr_to_csv.py
@ -854,6 +885,13 @@ parser = argparse.ArgumentParser()
parser.add_argument("files", nargs="+")
def main(files):
"""Files must be sorted lexicographically
Filenames must be <row >-<colum >.txt.
000-000.txt
000-001.txt
001-000.txt
etc...
"""
rows = []
for f in files:
directory, filename = os.path.split(f)
@ -871,9 +909,9 @@ def main(files):
if __name__ == "__main__":
args = parser.parse_args()
main(args.files)
files = args.files
files.sort()
main(files)
#+END_SRC
* Utils
@ -899,10 +937,6 @@ with ~advice-add~.
(concat "#+ATTR_HTML: :width " width " :height " height "\n[[file:" text " ]]")
#+END_SRC
#+RESULTS : html-image-size
#+ATTR_HTML : :width 100% :height 100%
[[file: ]]
#+BEGIN_SRC emacs-lisp :results none
(defun remove-attributes-from-src-block-result (&rest args)
(let ((location (org-babel-where-is-src-block-result))
Eric Ihli
5 years ago
