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image-table-ocr
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Add example image and csv output

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Eric Ihli 5 years ago
parent 4eca593944
commit 6891fc9990
3 changed files with 56 additions and 66 deletions
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57
pdf_table_extraction_and_ocr.org
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@ -22,6 +22,34 @@ output~ to a code block will minimize that noise.
* 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
** To get CSV data from a table in a scanned pdf document:
#+BEGIN_SRC shell :results none :session *Shell*
@ -367,7 +395,8 @@ 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.
#+BEGIN_SRC python :noweb-ref bounding-rects :results none
#+NAME: bounding-rects
#+BEGIN_SRC python :results none
contours, heirarchy = cv2.findContours(
mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE,
)
@ -409,7 +438,8 @@ 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.
#+BEGIN_SRC python :noweb-ref sort-contours :results none
#+NAME: sort-contours
#+BEGIN_SRC python :results none
def cell_in_same_row(c1, c2):
c1_center = c1[1] + c1[3] - c1[3] / 2
c2_bottom = c2[1] + c2[3]
@ -479,7 +509,8 @@ cv2.imwrite("resources/examples/example-table-cells-numbered.png", image)
#+ATTR_HTML: :width 500px :height 100%
[[file:resources/examples/example-table-cells-numbered.png]]
#+BEGIN_SRC python :noweb-ref extract-cells-from-table :noweb yes :eval no
#+NAME: extract-cells-from-table
#+BEGIN_SRC python :noweb yes :eval no
def extract_cell_images_from_table(image):
<<blur>>
<<threshold>>
@ -547,14 +578,16 @@ def crop_to_text(image):
# 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)
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
MIN_CHAR_AREA = 5 * 9
if bounding_rects:
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 [(x, y, w, h) for x, y, w, h in bounding_rects if w * h > MIN_CHAR_AREA]:
for x, y, w, h in char_sized_bounding_rects:
minx = min(minx, x)
miny = min(miny, y)
maxx = max(maxx, x + w)
@ -562,8 +595,8 @@ def crop_to_text(image):
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
# If we morphed out all of the text, assume an empty image.
cropped = MAX_COLOR_VAL * np.ones(shape=(20, 100), dtype=np.uint8)
bordered = cv2.copyMakeBorder(cropped, 5, 5, 5, 5, cv2.BORDER_CONSTANT, None, 255)
return bordered
#+END_SRC
@ -571,6 +604,7 @@ def crop_to_text(image):
#+HEADER: :post html-image-size(text=*this*, width="200px")
#+BEGIN_SRC python :noweb no-export :results raw :exports both
import cv2
import numpy as np
<<crop-to-text>>
image = cv2.imread("resources/examples/example-table-cell-1-1.png", cv2.IMREAD_GRAYSCALE)
image = crop_to_text(image)
@ -606,6 +640,7 @@ def ocr_image(image, config):
#+BEGIN_SRC python :noweb no-export :exports both
import pytesseract
import cv2
import numpy as np
image = cv2.imread("resources/examples/example-table-cell-1-1.png", cv2.IMREAD_GRAYSCALE)
<<crop-to-text>>
<<ocr-image>>
@ -884,6 +919,8 @@ if __name__ == "__main__":
import math
import cv2
import numpy as np
import pytesseract
<<crop-to-text>>
<<ocr-image>>
@ -902,7 +939,8 @@ import os
import sys
import cv2
import pytesseract
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.
@ -913,9 +951,6 @@ 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(image_file, tess_args):
directory, filename = os.path.split(image_file)
filename_sans_ext, ext = os.path.splitext(filename)

12
table_ocr/ocr_image/__init__.py
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@ -1,6 +1,8 @@
import math
import cv2
import numpy as np
import pytesseract
def crop_to_text(image):
MAX_COLOR_VAL = 255
@ -27,14 +29,16 @@ def crop_to_text(image):
# 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)
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
MIN_CHAR_AREA = 5 * 9
if bounding_rects:
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 [(x, y, w, h) for x, y, w, h in bounding_rects if w * h > MIN_CHAR_AREA]:
for x, y, w, h in char_sized_bounding_rects:
minx = min(minx, x)
miny = min(miny, y)
maxx = max(maxx, x + w)
@ -42,8 +46,8 @@ def crop_to_text(image):
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
# If we morphed out all of the text, assume an empty image.
cropped = MAX_COLOR_VAL * np.ones(shape=(20, 100), dtype=np.uint8)
bordered = cv2.copyMakeBorder(cropped, 5, 5, 5, 5, cv2.BORDER_CONSTANT, None, 255)
return bordered
def ocr_image(image, config):

53
table_ocr/ocr_image/__main__.py
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@ -4,7 +4,8 @@ import os
import sys
import cv2
import pytesseract
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.
@ -15,56 +16,6 @@ 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 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)
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
MIN_CHAR_AREA = 5 * 9
if bounding_rects:
minx, miny, maxx, maxy = math.inf, math.inf, 0, 0
for x, y, w, h in [(x, y, w, h) for x, y, w, h in bounding_rects if w * h > MIN_CHAR_AREA]:
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
def ocr_image(image, config):
return pytesseract.image_to_string(
image,
config=config
)
def main(image_file, tess_args):
directory, filename = os.path.split(image_file)
filename_sans_ext, ext = os.path.splitext(filename)

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