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image-table-ocr/pdf_table_extraction_and_oc...

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Org Mode

5 years ago
# -*- org-image-actual-width: 500; -*-
#+TITLE: PDF Parsing
#+PROPERTY: header-args :session *Python*
#+STARTUP: inlineimages
#+OPTIONS: ^:nil H:4
5 years ago
#+BEGIN_COMMENT
Some notes about the header for those not familiar with Org Mode:
The property `header-args` with ~:session \*Python\*~ will cause all evaluated
source code blocks to be evaluated in the buffer named "\*Python\*", which is the
default buffer name for the buffer connected to an inferior python process. This
is useful for interactive development. It gives you a REPL to work with rather
than having to constantly evaluate source code blocks and view the results
output to try any change.
Another note along those lines is that when source code blocks are evaluated,
some unnecessary output is printed in the ~*Python*~ buffer. Adding ~:results
output~ to a code block will minimize that noise.
#+END_COMMENT
* Overview
This Python package provides utilities for extracting tabular data from PDF
files and images of tables.
Given an image that contains a table...
#+ATTR_HTML: :width 25%
[[file:resources/examples/example-page.png]]
Extract the the text into a CSV format...
#+BEGIN_EXAMPLE
PRIZE,ODDS 1 IN:,# OF WINNERS*
$3,9.09,"282,447"
$5,16.66,"154,097"
$7,40.01,"64,169"
$10,26.67,"96,283"
$20,100.00,"25,677"
$30,290.83,"8,829"
$50,239.66,"10,714"
$100,919.66,"2,792"
$500,"6,652.07",386
"$40,000","855,899.99",3
1,i223,
Toa,,
,,
,,"* Based upon 2,567,700"
#+END_EXAMPLE
The package is split into modules with narrow focuses.
- ~pdf_to_images~ uses Poppler and ImageMagick to extract images from a PDF.
- ~extract_tables~ finds and extracts table-looking things from an image.
- ~extract_cells~ extracts and orders cells from a table.
- ~ocr_image~ uses Tesseract to turn a OCR the text from an image of a cell.
- ~ocr_to_csv~ converts into a CSV the directory structure that ~ocr_image~ outputs.
** Requirements
*** Python packages
- numpy
- opencv-python
- pytesseract
*** External
- ~pdfimages~ from Poppler
- Tesseract
- ~mogfrify~ ImageMagick
** Contributing
This package was created in a [[https://en.wikipedia.org/wiki/Literate_programming][literate programming]] style with the help of [[https://orgmode.org/worg/org-contrib/babel/intro.html][Babel]].
The unfortunate downside is the obscurity of the tooling. It creates a bit of a
barrier for contributors who aren't already familiar with Emacs and Babel.
** Example usage
Here is an example of a shell script that uses each module to turn a pdf with a
table into CSV output.
Depending on your needs, you may not need all of these steps. If you already
have an image of a table, you can jum straight to extracting the cells.
Each piece is its own python module, so you can also simply import the pieces
you need into your own python projects and use them as needed.
#+NAME: ocr_tables
#+BEGIN_SRC shell :results none :tangle ocr_tables :tangle-mode (identity #o755)
#!/bin/sh
PDF=$1
python -m table_ocr.pdf_to_images $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 {} | grep cells > /tmp/extracted-cells.txt
cat /tmp/extracted-cells.txt | xargs -I{} python -m table_ocr.ocr_image {} --psm 7 -l table-ocr
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
** Possible improvements
Detect text with the stroke-width-transform alogoritm. https://zablo.net/blog/post/stroke-width-transform-swt-python/index.html
* Preparing data
5 years ago
Not all pdfs need to be sent through OCR to extract the text content. If you can
click and drag to highlight text in the pdf, then the tools in this library
probably aren't necessary.
** Converting PDFs to images
5 years ago
This code calls out to [[https://manpages.debian.org/testing/poppler-utils/pdfimages.1.en.html][pdfimages]] from [[https://poppler.freedesktop.org/][Poppler]].
#+NAME: pdf-to-images
#+BEGIN_SRC python :results none
# Wrapper around the Poppler command line utility "pdfimages" and helpers for
# finding the output files of that command.
5 years ago
def pdf_to_images(pdf_filepath):
"""
Turn a pdf into images
"""
directory, filename = os.path.split(pdf_filepath)
with working_dir(directory):
image_filenames = pdfimages(pdf_filepath)
# Since pdfimages creates a number of files named each for there page number
# and doesn't return us the list that it created
return [os.path.join(directory, f) for f in image_filenames]
def pdfimages(pdf_filepath):
"""
Uses the `pdfimages` utility from Poppler
(https://poppler.freedesktop.org/). Creates images out of each page. Images
are prefixed by their name sans extension and suffixed by their page number.
This should work up to pdfs with 999 pages since find matching files in dir
uses 3 digits in its regex.
5 years ago
"""
directory, filename = os.path.split(pdf_filepath)
filename_sans_ext = filename.split(".pdf")[0]
subprocess.run(["pdfimages", "-png", pdf_filepath, filename.split(".pdf")[0]])
image_filenames = find_matching_files_in_dir(filename_sans_ext, directory)
logger.debug(
"Converted {} into files:\n{}".format(pdf_filepath, "\n".join(image_filenames))
)
5 years ago
return image_filenames
def find_matching_files_in_dir(file_prefix, directory):
files = [
filename
for filename in os.listdir(directory)
if re.match(r"{}-\d{{3}}.*\.png".format(re.escape(file_prefix)), filename)
5 years ago
]
return files
#+END_SRC
** Detecting image orientation and applying rotation.
Tesseract can detect orientation and we can then use [[https://www.imagemagick.org/script/mogrify.php][ImageMagick's mogrify]] to
rotate the image.
Here's an example of the output we get from orientation detection with
Tesseract.
#+BEGIN_EXAMPLE
➜ example/ tesseract --psm 0 example-000.png -
Page number: 0
Orientation in degrees: 90
Rotate: 270
Orientation confidence: 26.86
Script: Latin
Script confidence: 2.44
#+END_EXAMPLE
The following are some helpers to detect orientation of the images that Poppler
extracted and, if the images are rotated or skewed, use ImageMagick's `mogrify`
to correct the rotation. This makes OCR more straightforward.
5 years ago
#+NAME: fix-orientation
#+BEGIN_SRC python :results none
def preprocess_img(filepath):
"""
Processing that involves running shell executables,
like mogrify to rotate.
"""
rotate = get_rotate(filepath)
logger.debug("Rotating {} by {}.".format(filepath, rotate))
mogrify(filepath, rotate)
def get_rotate(image_filepath):
output = (
subprocess.check_output(["tesseract", "--psm", "0", image_filepath, "-"])
.decode("utf-8")
.split("\n")
)
output = next(l for l in output if "Rotate: " in l)
output = output.split(": ")[1]
return output
def mogrify(image_filepath, rotate):
subprocess.run(["mogrify", "-rotate", rotate, image_filepath])
#+END_SRC
* Detecting tables
This answer from opencv.org was heavily referenced while writing the code around
table detection:
https://answers.opencv.org/question/63847/how-to-extract-tables-from-an-image/.
It's much easier to OCR a table when the table is the only thing in the image.
This code detects tables in an image and returns a list of images of just the
tables, no surrounding text or noise.
The blurring, thresholding, and line detection is used here as well as later on
for cell extraction. They are good techniques for cleaning an image up in a way
that makes things like shape detection more accurate.
#+NAME: detect-tables
#+BEGIN_SRC python :results none :noweb yes
5 years ago
def find_tables(image):
<<blur>>
<<threshold>>
<<lines-of-table>>
contours, heirarchy = cv2.findContours(
mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE,
)
MIN_TABLE_AREA = 1e5
contours = [c for c in contours if cv2.contourArea(c) > MIN_TABLE_AREA]
perimeter_lengths = [cv2.arcLength(c, True) for c in contours]
epsilons = [0.1 * p for p in perimeter_lengths]
approx_polys = [cv2.approxPolyDP(c, e, True) for c, e in zip(contours, epsilons)]
bounding_rects = [cv2.boundingRect(a) for a in approx_polys]
# 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.
# A table should have a lot of intersections. We might have a rectangular image
# here though which would only have 4 intersections, 1 at each corner.
# Leaving that step as a future TODO if it is ever necessary.
images = [image[y:y+h, x:x+w] for x, y, w, h in bounding_rects]
return images
#+END_SRC
Here is an the an example of the result of the ~find_tables~ function.
5 years ago
#+HEADER: :post html-image-size(text=*this*, width="500px")
#+BEGIN_SRC python :noweb-ref test-detect-table :noweb strip-export :results none
5 years ago
import cv2
<<detect-tables>>
5 years ago
image_filename = "resources/examples/example-page.png"
image = cv2.imread(image_filename, cv2.IMREAD_GRAYSCALE)
image = find_tables(image)[0]
cv2.imwrite("resources/examples/example-table.png", image)
#+END_SRC
#+BEGIN_CENTER
#+ATTR_HTML: :width 250px
[[file:resources/examples/example-page.png]]
#+ATTR_HTML: :width 250px
[[file:resources/examples/example-table.png]]
#+END_CENTER
** Improving accuracy
It's likely that some images will contain tables that aren't accurately
recognized by the code above. The code will then need to be made more robust.
But how will we know that changes to the code don't break the detection of
tables that were previously detected?
It might be good to add some type of test suite in the future that contains a
spec that matches a pdf with the pages and pixel coordinates of the detected
tables. The coordinates would need to have a range. Something like
"example-1.pdf, page-2.png, [450:470, 200:210, 800:820, 1270:1290]" where the
elements of the list are valid x, y, w, h ranges. So the test will pass if if
the x, y, width and height are anywhere in that range.
5 years ago
* OCR tables
Tesseract does not perform well when run on images of tables. It performs best
when given a single line of text with no extra noise.
Therefore, our next task is to find and extract the bounding box of each cell in
the table. Run tesseract on each cell. Print a comma seperated output.
5 years ago
We'll start with an image shown at the end of the previous section.
** Training Tesseract
It's a very good idea to train tesseract. Accuracy will improve tremendously.
Clone the tesstrain repo at [[https://github.com/tesseract-ocr/tesstrain]].
Run the [[ocr_tables][~ocr_tables~]] script on a few pdfs to generate some training data. That
script outputs pairs of ~.png~ and ~.gt.txt~ files that can be used by
tesstrain.
Make sure the ~.gt.txt~ files contain an accurate recognition of the
corresponding image. Since the first few runs will be untrained, you'll probably
need to fix up a few of the text files.
Once they are accurate, move them to a new subdirectory of the tesstrain repo;
~tesstrain/data/<model-name>-ground-truth/~.
You'll also need to clone the ~tessdata_best~ repo,
[[https://github.com/tesseract-ocr/tessdata_best]] and the
https://github.com/tesseract-ocr/langdata to use as the start of the
training model.
I'm actually not sure how much the punctuation and numbers from ~langdata~ help.
I didn't keep accurate records while playing with the training, I don't
thoroughly understand it, and it's not profitable for me to explore it at the
moment. It worked for my purposes and that has been good enough.
#+BEGIN_EXAMPLE
make training MODEL_NAME=table-ocr START_MODEL=eng TESSDATA=~/src/tessdata_best PUNC_FILE=~/src/langdata/eng/eng.punc NUMBERS_FILE=~/src/langdata/eng/eng.numbers
#+END_EXAMPLE
Once the training is complete, there will be a new file
~tesstrain/data/<model-name>.traineddata~. Copy that file to the directory
Tesseract searches for models. On my machine, it was ~/usr/local/share/tessdata/~.
** Blur
5 years ago
Blurring helps to make noise less noisy so that the overall structure of an
image is more detectable.
That gray row at the bottom is kind of noisy. If we don't somehow clean it up,
OpenCV will detect all sorts of odd shapes in there and it will throw off our
cell detection.
Cleanup can be accomplished with a blur followed by some thresholding.
#+BEGIN_SRC python :noweb-ref blur :results none
BLUR_KERNEL_SIZE = (17, 17)
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)
#+END_SRC
#+HEADER: :post html-image-size(text=*this*, width="500px")
#+BEGIN_SRC python :noweb no-export :results none :exports both
5 years ago
image = ~cv2.imread("resources/examples/example-table.png", cv2.IMREAD_GRAYSCALE)
<<blur>>
cv2.imwrite("resources/examples/example-table-blurred.png", blurred)
#+END_SRC
#+ATTR_HTML: :width 500px :height 100%
[[file:resources/examples/example-table-blurred.png]]
** Threshold
5 years ago
We've got a bunch of pixels that are gray. Thresholding will turn them all
either black or white. Having all black or white pixels lets us do morphological
transformations like erosion and dilation.
#+BEGIN_SRC python :noweb-ref threshold :results none
MAX_COLOR_VAL = 255
BLOCK_SIZE = 15
SUBTRACT_FROM_MEAN = -2
img_bin = cv2.adaptiveThreshold(
~blurred,
MAX_COLOR_VAL,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY,
BLOCK_SIZE,
SUBTRACT_FROM_MEAN,
)
#+END_SRC
#+HEADER: :post html-image-size(text=*this*, width="500px")
#+BEGIN_SRC python :noweb no-export :results none :exports both
5 years ago
<<threshold>>
cv2.imwrite("resources/examples/example-table-thresholded.png", img_bin)
#+END_SRC
#+ATTR_HTML: :width 500px :height 100%
[[file:resources/examples/example-table-thresholded.png]]
** Finding the vertical and horizontal lines of the table
5 years ago
#+BEGIN_SRC python :noweb-ref lines-of-table :results none
vertical = horizontal = img_bin.copy()
SCALE = 5
image_width, image_height = horizontal.shape
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (int(image_width / SCALE), 1))
horizontally_opened = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, horizontal_kernel)
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, int(image_height / SCALE)))
vertically_opened = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, vertical_kernel)
horizontally_dilated = cv2.dilate(horizontally_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (40, 1)))
vertically_dilated = cv2.dilate(vertically_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (1, 60)))
mask = horizontally_dilated + vertically_dilated
#+END_SRC
Note: There's a wierd issue with the results of the example below when it's
evaluated as part of an export or a full-buffer evaluation. If you evaluate the
example by itself, it looks the way it's intended. If you evaluate it as part of
an entire buffer evaluation, like during export, it's distorted.
5 years ago
#+HEADER: :post html-image-size(text=*this*, width="500px")
#+BEGIN_SRC python :noweb no-export :results none :exports both
5 years ago
<<lines-of-table>>
cv2.imwrite("resources/examples/example-table-lines.png", mask)
#+END_SRC
#+ATTR_HTML: :width 500px
5 years ago
[[file:resources/examples/example-table-lines.png]]
** Finding the contours
5 years ago
Blurring and thresholding allow us to find the lines. Opening the lines allows
us to find the contours.
An "Opening" is an erosion followed by a dilation. Great examples and
descriptions of each morphological operation can be found at
[[https://docs.opencv.org/trunk/d9/d61/tutorial_py_morphological_ops.html][https://docs.opencv.org/trunk/d9/d61/tutorial_py_morphological_ops.html]].
#+BEGIN_QUOTE
Contours can be explained simply as a curve joining all the continuous points
(along the boundary), having same color or intensity. The contours are a useful
tool for shape analysis and object detection and recognition.
#+END_QUOTE
We can search those contours to find rectangles of certain size.
To do that, we can use OpenCV's ~approxPolyEP~ function. It takes as arguments
the contour (list of contiguous points), and a number representing how different
the polygon perimeter length can be from the true perimeter length of the
contour. ~0.1~ (10%) seems to be a good value. The difference in perimeter
length between a 4-sided polygon and a 3-sided polygon is greater than 10% and
the difference between a 5+ sided polygon and a 4-sided polygon is less than
10%. So a 4-sided polygon is the polygon with the fewest sides that leaves the
difference in perimeter length within our 10% threshold.
Then we just get the bounding rectangle of that polygon and we have our cells!
We might need to do a little more filtering of those rectangles though. We might
have accidentally found some noise such as another image on the page or a title
header bar or something. If we know our cells are all within a certain size (by
area of pixels) then we can filter out the junk cells by removing cells
above/below certain sizes.
#+NAME: bounding-rects
#+BEGIN_SRC python :results none
5 years ago
contours, heirarchy = cv2.findContours(
mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE,
)
perimeter_lengths = [cv2.arcLength(c, True) for c in contours]
epsilons = [0.05 * p for p in perimeter_lengths]
approx_polys = [cv2.approxPolyDP(c, e, True) for c, e in zip(contours, epsilons)]
# Filter out contours that aren't rectangular. Those that aren't rectangular
# are probably noise.
approx_rects = [p for p in approx_polys if len(p) == 4]
bounding_rects = [cv2.boundingRect(a) for a in approx_polys]
# Filter out rectangles that are too narrow or too short.
MIN_RECT_WIDTH = 40
MIN_RECT_HEIGHT = 10
bounding_rects = [
r for r in bounding_rects if MIN_RECT_WIDTH < r[2] and MIN_RECT_HEIGHT < r[3]
]
# The largest bounding rectangle is assumed to be the entire table.
# Remove it from the list. We don't want to accidentally try to OCR
# the entire table.
largest_rect = max(bounding_rects, key=lambda r: r[2] * r[3])
bounding_rects = [b for b in bounding_rects if b is not largest_rect]
cells = [c for c in bounding_rects]
#+END_SRC
** Sorting the bounding rectangles
5 years ago
We want to process these from left-to-right, top-to-bottom.
I've thought of a straightforward algorithm for it, but it could probably be
made more efficient.
We'll find the most rectangle with the most top-left corner. 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. Then we'll sort those rectangles by the x
value of their center. We'll remove those rectangles from the list and repeat.
#+NAME: sort-contours
#+BEGIN_SRC python :results none
5 years ago
def cell_in_same_row(c1, c2):
c1_center = c1[1] + c1[3] - c1[3] / 2
c2_bottom = c2[1] + c2[3]
c2_top = c2[1]
return c2_top < c1_center < c2_bottom
orig_cells = [c for c in cells]
rows = []
while cells:
first = cells[0]
rest = cells[1:]
cells_in_same_row = sorted(
[
c for c in rest
if cell_in_same_row(c, first)
],
key=lambda c: c[0]
)
row_cells = sorted([first] + cells_in_same_row, key=lambda c: c[0])
rows.append(row_cells)
cells = [
c for c in rest
if not cell_in_same_row(c, first)
]
# Sort rows by average height of their center.
def avg_height_of_center(row):
centers = [y + h - h / 2 for x, y, w, h in row]
return sum(centers) / len(centers)
rows.sort(key=avg_height_of_center)
#+END_SRC
To test if this code works, let's try sorting the bounding rectangles and
numbering them from right to left, top to bottom.
#+HEADER: :post html-image-size(text=*this*, width="500px")
#+BEGIN_SRC python :noweb no-export :results none :exports both
5 years ago
import cv2
image = cv2.imread("resources/examples/example-table.png", cv2.IMREAD_GRAYSCALE)
<<blur>>
<<threshold>>
<<lines-of-table>>
<<bounding-rects>>
<<sort-contours>>
FONT_SCALE = 0.7
FONT_COLOR = (127, 127, 127)
for i, row in enumerate(rows):
for j, cell in enumerate(row):
x, y, w, h = cell
cv2.putText(
image,
"{},{}".format(i, j),
(int(x + w - w / 2), int(y + h - h / 2)),
cv2.FONT_HERSHEY_SIMPLEX,
FONT_SCALE,
FONT_COLOR,
2,
)
cv2.imwrite("resources/examples/example-table-cells-numbered.png", image)
#+END_SRC
#+ATTR_HTML: :width 500px :height 100%
[[file:resources/examples/example-table-cells-numbered.png]]
#+NAME: extract-cells-from-table
#+BEGIN_SRC python :noweb yes :eval no
5 years ago
def extract_cell_images_from_table(image):
<<blur>>
<<threshold>>
<<lines-of-table>>
<<bounding-rects>>
<<sort-contours>>
cell_images_rows = []
for row in rows:
cell_images_row = []
for x, y, w, h in row:
cell_images_row.append(image[y:y+h, x:x+w])
cell_images_rows.append(cell_images_row)
return cell_images_rows
#+END_SRC
#+HEADER: :post html-image-size(text=*this*, width="200px")
#+BEGIN_SRC python :noweb no-export :results none :exports both
5 years ago
<<extract-cells-from-table>>
image = cv2.imread("resources/examples/example-table.png", cv2.IMREAD_GRAYSCALE)
cell_images_rows = extract_cell_images_from_table(image)
cv2.imwrite("resources/examples/example-table-cell-1-1.png", cell_images_rows[1][1])
#+END_SRC
#+ATTR_HTML: :width 200px :height 100%
[[file:resources/examples/example-table-cell-1-1.png]]
** Cropping each cell to the text
5 years ago
OCR with Tesseract works best when there is about 10 pixels of white border
around the text.
Our bounding rectangles may have picked up some stray pixels from the horizontal
and vertical lines of the cells in the table. It's probobly just a few pixels,
much fewer than the width of the text. If that's the case, then we can remove
that noise with a simple open morph.
Once the stray border pixels have been removed, we can expand our border using
~copyMakeBorder~.
5 years ago
#+BEGIN_SRC python :eval no :noweb-ref crop-to-text
def crop_to_text(image):
MAX_COLOR_VAL = 255
BLOCK_SIZE = 15
SUBTRACT_FROM_MEAN = -2
img_bin = cv2.adaptiveThreshold(
~image,
MAX_COLOR_VAL,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY,
BLOCK_SIZE,
SUBTRACT_FROM_MEAN,
)
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)
opened = cv2.dilate(opened, kernel)
5 years ago
contours, hierarchy = cv2.findContours(opened, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
5 years ago
bounding_rects = [cv2.boundingRect(c) for c in contours]
NUM_PX_COMMA = 6
MIN_CHAR_AREA = 5 * 9
char_sized_bounding_rects = [(x, y, w, h) for x, y, w, h in bounding_rects if w * h > MIN_CHAR_AREA]
if char_sized_bounding_rects:
minx, miny, maxx, maxy = math.inf, math.inf, 0, 0
for x, y, w, h in char_sized_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, assume an empty image.
cropped = MAX_COLOR_VAL * np.ones(shape=(20, 100), dtype=np.uint8)
5 years ago
bordered = cv2.copyMakeBorder(cropped, 5, 5, 5, 5, cv2.BORDER_CONSTANT, None, 255)
return bordered
#+END_SRC
#+HEADER: :post html-image-size(text=*this*, width="200px")
#+BEGIN_SRC python :noweb no-export :results none :exports both
5 years ago
import cv2
import numpy as np
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<<crop-to-text>>
image = cv2.imread("resources/examples/example-table-cell-1-1.png", cv2.IMREAD_GRAYSCALE)
image = crop_to_text(image)
cv2.imwrite("resources/examples/example-table-cell-1-1-cropped.png", image)
#+END_SRC
#+ATTR_HTML: :width 200px :height 100%
[[file:resources/examples/example-table-cell-1-1-cropped.png]]
** OCR each cell
5 years ago
If we cleaned up the images well enough, we might get some accurate OCR!
There is plenty that could have gone wrong along the way.
The first step to troubleshooting is to view the intermediate images and see if
there's something about your image that is obviously abnormal, like some really
thick noise or a wrongly detected table.
If everything looks reasonable but the OCR is doing something like turning a
period into a comma, then you might need to do some custom Tesseract training.
#+BEGIN_SRC python :noweb-ref ocr-image :eval no
5 years ago
def ocr_image(image, config):
return pytesseract.image_to_string(
image,
5 years ago
config=config
)
#+END_SRC
#+BEGIN_SRC python :noweb no-export :exports both
import pytesseract
import cv2
import numpy as np
5 years ago
image = cv2.imread("resources/examples/example-table-cell-1-1.png", cv2.IMREAD_GRAYSCALE)
<<crop-to-text>>
5 years ago
<<ocr-image>>
image = crop_to_text(image)
5 years ago
ocr_image(image, "--psm 7")
#+END_SRC
#+RESULTS:
: 9.09
* Files
:PROPERTIES:
:header-args: :mkdirp yes :noweb yes
:END:
#+BEGIN_SRC python :tangle table_ocr/__init__.py :mkdirp yes :results none
5 years ago
#+END_SRC
** setup.py
#+BEGIN_SRC python :tangle setup.py :results none
import setuptools
long_description = """
Utilities for turning images of tables into CSV data. Uses Tesseract and OpenCV.
5 years ago
Requires binaries for tesseract and pdfimages (from Poppler).
"""
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setuptools.setup(
name="table_ocr",
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version="0.0.1",
author="Eric Ihli",
author_email="eihli@owoga.com",
description="Turn images of tables into CSV data.",
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long_description=long_description,
long_description_content_type="text/plain",
url="https://github.com/eihli/image-table-ocr",
5 years ago
packages=setuptools.find_packages(),
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
install_requires=[
"pytesseract~=0.3",
"opencv-python~=4.2",
],
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python_requires='>=3.6',
)
#+END_SRC
** table_ocr
*** table_ocr/__init__.py
#+BEGIN_SRC python :tangle table_ocr/__init__.py :results none :exports none
5 years ago
#+END_SRC
*** table_ocr/util.py
5 years ago
#+BEGIN_SRC python :tangle table_ocr/util.py :results none
5 years ago
from contextlib import contextmanager
import functools
import logging
import os
import tempfile
<<get-logger>>
5 years ago
@contextmanager
def working_dir(directory):
original_working_dir = os.getcwd()
try:
os.chdir(directory)
yield directory
finally:
os.chdir(original_working_dir)
def make_tempdir(identifier):
return tempfile.mkdtemp(prefix="{}_".format(identifier))
#+END_SRC
*** table_ocr/pdf_to_images/
**** table_ocr/pdf_to_images/__init__.py
#+NAME: pdf_to_images/__init__.py
#+HEADER: :tangle table_ocr/pdf_to_images/__init__.py
#+BEGIN_SRC python :results none
import os
import re
import subprocess
from table_ocr.util import get_logger, working_dir
logger = get_logger(__name__)
<<pdf-to-images>>
5 years ago
<<fix-orientation>>
#+END_SRC
**** table_ocr/pdf_to_images/__main__.py
5 years ago
Takes a variable number of pdf files and creates images out of each page of the
file using ~pdfimages~ from Poppler. Images are created in the same directory
that contains the pdf.
Prints each pdf followed by the images extracted from that pdf followed by a
blank line.
#+BEGIN_SRC shell :eval no :exports code
python -m table_ocr.prepare_pdfs /tmp/file1/file1.pdf /tmp/file2/file2.pdf ...
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#+END_SRC
#+NAME: pdf_to_images/__main__.py
#+HEADER: :tangle table_ocr/pdf_to_images/__main__.py
#+BEGIN_SRC python
5 years ago
import argparse
from table_ocr.util import working_dir, make_tempdir, get_logger
from table_ocr.pdf_to_images import pdf_to_images, preprocess_img
5 years ago
logger = get_logger(__name__)
5 years ago
parser = argparse.ArgumentParser()
parser.add_argument("files", nargs="+")
5 years ago
def main(files):
pdf_images = []
for f in files:
pdf_images.append((f, pdf_to_images(f)))
for pdf, images in pdf_images:
for image in images:
preprocess_img(image)
for pdf, images in pdf_images:
print("{}\n{}\n".format(pdf, "\n".join(images)))
if __name__ == "__main__":
args = parser.parse_args()
main(args.files)
#+END_SRC
*** table_ocr/extract_tables/
**** table_ocr/extract_tables/__init__.py
#+NAME: extract_tables/__init__.py
#+HEADER: :tangle table_ocr/extract_tables/__init__.py
#+BEGIN_SRC python
import cv2
<<detect-tables>>
#+END_SRC
**** table_ocr/extract_tables/__main__.py
5 years ago
Takes 1 or more image paths as arguments.
5 years ago
Images are opened and read with OpenCV.
Tables are detected and extracted to a new subdirectory of the given image. The
subdirectory will be the filename sans the extension. The tables inside that
directory will be named ~table-000.png~.
If you want to do something with the output, like pipe the paths of the
extracted tables into some other utility, here is a description of the output.
For each image path given as an agument, outputs:
1. The given image path
2. Paths of extracted tables; seperated by newlines
3. Empty newline
5 years ago
#+NAME: extract_tables/__main__.py
#+BEGIN_SRC python :tangle table_ocr/extract_tables/__main__.py :results none
5 years ago
import argparse
import os
import cv2
from table_ocr.extract_tables import find_tables
5 years ago
parser = argparse.ArgumentParser()
parser.add_argument("files", nargs="+")
def main(files):
results = []
for f in files:
directory, filename = os.path.split(f)
image = cv2.imread(f, cv2.IMREAD_GRAYSCALE)
tables = find_tables(image)
files = []
filename_sans_extension = os.path.splitext(filename)[0]
if tables:
os.makedirs(os.path.join(directory, filename_sans_extension), exist_ok=True)
5 years ago
for i, table in enumerate(tables):
table_filename = "table-{:03d}.png".format(i)
table_filepath = os.path.join(
directory, filename_sans_extension, table_filename
)
5 years ago
files.append(table_filepath)
cv2.imwrite(table_filepath, table)
if tables:
results.append((f, files))
5 years ago
for image_filename, table_filenames in results:
print("\n".join(table_filenames))
5 years ago
if __name__ == "__main__":
args = parser.parse_args()
files = args.files
main(files)
#+END_SRC
*** table_ocr/extract_cells/
**** table_ocr/extract_cells/__init__.py
#+BEGIN_SRC python :tangle table_ocr/extract_cells/__init__.py
import cv2
5 years ago
<<extract-cells-from-table>>
5 years ago
#+END_SRC
**** table_ocr/extract_cells/__main__.py
Takes as a command line argument a path to an image of a table.
Detects cells in the table and extracts each cell to an image file in a new
~/cells/~ subdirectory in the same directory of the given image's path.
Each cell filename is suffixed with ~<row>-<column>~ so that the filenames can
be sorted lexicographically and will align with reading the cells from
left-to-right, top-to-bottom.
Prints to stdout the lexicographically sorted list of filenames of the extracted
cells.
#+BEGIN_SRC python :tangle table_ocr/extract_cells/__main__.py :results none
5 years ago
import os
import sys
import cv2
from table_ocr.extract_cells import extract_cell_images_from_table
5 years ago
def main(f):
results = []
directory, filename = os.path.split(f)
table = cv2.imread(f, cv2.IMREAD_GRAYSCALE)
rows = extract_cell_images_from_table(table)
cell_img_dir = os.path.join(directory, "cells")
os.makedirs(cell_img_dir, exist_ok=True)
for i, row in enumerate(rows):
for j, cell in enumerate(row):
cell_filename = "{:03d}-{:03d}.png".format(i, j)
path = os.path.join(cell_img_dir, cell_filename)
cv2.imwrite(path, cell)
print(path)
5 years ago
<<extract-cells-from-table>>
if __name__ == "__main__":
main(sys.argv[1])
#+END_SRC
*** table_ocr/ocr_image/
**** table_ocr/ocr_image/__init__.py
#+BEGIN_SRC python :tangle table_ocr/ocr_image/__init__.py
import math
import cv2
import numpy as np
import pytesseract
<<crop-to-text>>
<<ocr-image>>
#+END_SRC
**** table_ocr/ocr_image/__main__.py
This does a little bit of cleanup before sending it through tesseract.
Creates images and text files that can be used for training tesseract. See
https://github.com/tesseract-ocr/tesstrain.
#+BEGIN_SRC python :tangle table_ocr/ocr_image/__main__.py :mkdirp yes :results none
import argparse
import math
import os
import sys
import cv2
from table_ocr.ocr_image import crop_to_text, ocr_image
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")
def main(image_file, tess_args):
directory, filename = os.path.split(image_file)
filename_sans_ext, ext = os.path.splitext(filename)
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, " ".join(tess_args))
print(txt)
with open(out_txtpath, "w") as txt_file:
txt_file.write(txt)
if __name__ == "__main__":
args, tess_args = parser.parse_known_args()
main(args.image, tess_args)
#+END_SRC
*** table_ocr/ocr_to_csv/
**** table_ocr/ocr_to_csv/__init__.py
#+BEGIN_SRC python :tangle table_ocr/ocr_to_csv/__init__.py
import csv
import io
import os
def text_files_to_csv(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)
with open(f) as of:
txt = of.read()
row, column = map(int, filename.split(".")[0].split("-"))
if row == len(rows):
rows.append([])
rows[row].append(txt)
csv_file = io.StringIO()
writer = csv.writer(csv_file)
writer.writerows(rows)
return csv_file.getvalue()
#+END_SRC
**** table_ocr/ocr_to_csv/__main__.py
#+BEGIN_SRC python :tangle table_ocr/ocr_to_csv/__main__.py
import argparse
import os
from table_ocr.ocr_to_csv import text_files_to_csv
parser = argparse.ArgumentParser()
parser.add_argument("files", nargs="+")
def main(files):
print(text_files_to_csv(files))
if __name__ == "__main__":
args = parser.parse_args()
files = args.files
files.sort()
main(files)
#+END_SRC
5 years ago
* Utils
The following code lets us specify a size for images when they are exported to
html.
Org supports specifying an export size for an image by putting the ~#+ATTR_HTML:
:width 100px~ before the image. But since our images are in a results drawer, we
need a way for our results drawer to do that for us automatically.
Adding ~#+ATTR_HTML~ after the beginning of the result block introduces a new
problem. Org-babel no longer recognizes the result as a result block and doesn't
remove it when a src block is re-evaluated, so we end up just appending new
results on each evaluation.
There is nothing configurable that will tell org-babel to remove our line. But
we can define a function to do some cleanup and then add it as a before hook
with ~advice-add~.
#+NAME: html-image-size
#+BEGIN_SRC emacs-lisp :var text="" :var width="100%" :var height="100%" :results raw :export code
5 years ago
(concat "#+ATTR_HTML: :width " width " :height " height "\n[[file:" text "]]")
#+END_SRC
#+BEGIN_SRC emacs-lisp :results none
(defun remove-attributes-from-src-block-result (&rest args)
(let ((location (org-babel-where-is-src-block-result))
(attr-regexp "[ ]*#\\+ATTR.*$"))
(when location
(save-excursion
(goto-char location)
(when (looking-at (concat org-babel-result-regexp ".*$"))
(next-line)
(while (looking-at attr-regexp)
(kill-whole-line)))))))
(advice-add 'org-babel-remove-result :before #'remove-attributes-from-src-block-result)
(advice-add 'org-babel-execute-src-block :before #'remove-attributes-from-src-block-result)
#+END_SRC
** Logging
#+BEGIN_SRC python :eval query :noweb-ref get-logger
def get_logger(name):
logger = logging.getLogger(name)
lvl = os.environ.get("PY_LOG_LVL", "info").upper()
handler = logging.StreamHandler()
formatter = logging.Formatter(logging.BASIC_FORMAT)
handler.setFormatter(formatter)
logger.addHandler(handler)
handler.setLevel(lvl)
logger.setLevel(lvl)
return logger
#+END_SRC