Repository: jasonlfunk/ocr-text-extraction Branch: master Commit: 2ca74e9faf57 Files: 4 Total size: 11.2 KB Directory structure: gitextract_leghq8u1/ ├── .gitignore ├── LICENSE ├── README.md └── extract_text ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ *.py[co] # Packages *.egg *.egg-info dist build eggs parts bin var sdist develop-eggs .installed.cfg # Installer logs pip-log.txt # Unit test / coverage reports .coverage .tox #Translations *.mo #Mr Developer .mr.developer.cfg ================================================ FILE: LICENSE ================================================ Copyright (c) 2012, Jason Funk Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ ocr-text-extraction =================== I am not actively supporting this script. It was just an experiment. Processes an image to extract the text portions. Primarily used for pre-processing for performing OCR. Implemented in Python using OpenCV. Based on the paper "Font and Background Color Independent Text Binarization" by T Kasar, J Kumar and A G Ramakrishnan http://www.m.cs.osakafu-u.ac.jp/cbdar2007/proceedings/papers/O1-1.pdf Copyright (c) 2012, Jason Funk ================================================ FILE: extract_text ================================================ #!/usr/bin/python # Processes an image to extract the text portions. Primarily # used for pre-processing for performing OCR. # Based on the paper "Font and Background Color Independent Text Binarization" by # T Kasar, J Kumar and A G Ramakrishnan # http://www.m.cs.osakafu-u.ac.jp/cbdar2007/proceedings/papers/O1-1.pdf # Copyright (c) 2012, Jason Funk # Permission is hereby granted, free of charge, to any person obtaining a copy of this software # and associated documentation files (the "Software"), to deal in the Software without restriction, # including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, # subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all copies or substantial # portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT # LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, # WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. import cv2 import numpy as np import sys import os.path if len(sys.argv) != 3: print "%s input_file output_file" % (sys.argv[0]) sys.exit() else: input_file = sys.argv[1] output_file = sys.argv[2] if not os.path.isfile(input_file): print "No such file '%s'" % input_file sys.exit() DEBUG = 0 # Determine pixel intensity # Apparently human eyes register colors differently. # TVs use this formula to determine # pixel intensity = 0.30R + 0.59G + 0.11B def ii(xx, yy): global img, img_y, img_x if yy >= img_y or xx >= img_x: #print "pixel out of bounds ("+str(y)+","+str(x)+")" return 0 pixel = img[yy][xx] return 0.30 * pixel[2] + 0.59 * pixel[1] + 0.11 * pixel[0] # A quick test to check whether the contour is # a connected shape def connected(contour): first = contour[0][0] last = contour[len(contour) - 1][0] return abs(first[0] - last[0]) <= 1 and abs(first[1] - last[1]) <= 1 # Helper function to return a given contour def c(index): global contours return contours[index] # Count the number of real children def count_children(index, h_, contour): # No children if h_[index][2] < 0: return 0 else: #If the first child is a contour we care about # then count it, otherwise don't if keep(c(h_[index][2])): count = 1 else: count = 0 # Also count all of the child's siblings and their children count += count_siblings(h_[index][2], h_, contour, True) return count # Quick check to test if the contour is a child def is_child(index, h_): return get_parent(index, h_) > 0 # Get the first parent of the contour that we care about def get_parent(index, h_): parent = h_[index][3] while not keep(c(parent)) and parent > 0: parent = h_[parent][3] return parent # Count the number of relevant siblings of a contour def count_siblings(index, h_, contour, inc_children=False): # Include the children if necessary if inc_children: count = count_children(index, h_, contour) else: count = 0 # Look ahead p_ = h_[index][0] while p_ > 0: if keep(c(p_)): count += 1 if inc_children: count += count_children(p_, h_, contour) p_ = h_[p_][0] # Look behind n = h_[index][1] while n > 0: if keep(c(n)): count += 1 if inc_children: count += count_children(n, h_, contour) n = h_[n][1] return count # Whether we care about this contour def keep(contour): return keep_box(contour) and connected(contour) # Whether we should keep the containing box of this # contour based on it's shape def keep_box(contour): xx, yy, w_, h_ = cv2.boundingRect(contour) # width and height need to be floats w_ *= 1.0 h_ *= 1.0 # Test it's shape - if it's too oblong or tall it's # probably not a real character if w_ / h_ < 0.1 or w_ / h_ > 10: if DEBUG: print "\t Rejected because of shape: (" + str(xx) + "," + str(yy) + "," + str(w_) + "," + str(h_) + ")" + \ str(w_ / h_) return False # check size of the box if ((w_ * h_) > ((img_x * img_y) / 5)) or ((w_ * h_) < 15): if DEBUG: print "\t Rejected because of size" return False return True def include_box(index, h_, contour): if DEBUG: print str(index) + ":" if is_child(index, h_): print "\tIs a child" print "\tparent " + str(get_parent(index, h_)) + " has " + str( count_children(get_parent(index, h_), h_, contour)) + " children" print "\thas " + str(count_children(index, h_, contour)) + " children" if is_child(index, h_) and count_children(get_parent(index, h_), h_, contour) <= 2: if DEBUG: print "\t skipping: is an interior to a letter" return False if count_children(index, h_, contour) > 2: if DEBUG: print "\t skipping, is a container of letters" return False if DEBUG: print "\t keeping" return True # Load the image orig_img = cv2.imread(input_file) # Add a border to the image for processing sake img = cv2.copyMakeBorder(orig_img, 50, 50, 50, 50, cv2.BORDER_CONSTANT) # Calculate the width and height of the image img_y = len(img) img_x = len(img[0]) if DEBUG: print "Image is " + str(len(img)) + "x" + str(len(img[0])) #Split out each channel blue, green, red = cv2.split(img) # Run canny edge detection on each channel blue_edges = cv2.Canny(blue, 200, 250) green_edges = cv2.Canny(green, 200, 250) red_edges = cv2.Canny(red, 200, 250) # Join edges back into image edges = blue_edges | green_edges | red_edges # Find the contours contours, hierarchy = cv2.findContours(edges.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) hierarchy = hierarchy[0] if DEBUG: processed = edges.copy() rejected = edges.copy() # These are the boxes that we are determining keepers = [] # For each contour, find the bounding rectangle and decide # if it's one we care about for index_, contour_ in enumerate(contours): if DEBUG: print "Processing #%d" % index_ x, y, w, h = cv2.boundingRect(contour_) # Check the contour and it's bounding box if keep(contour_) and include_box(index_, hierarchy, contour_): # It's a winner! keepers.append([contour_, [x, y, w, h]]) if DEBUG: cv2.rectangle(processed, (x, y), (x + w, y + h), (100, 100, 100), 1) cv2.putText(processed, str(index_), (x, y - 5), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255)) else: if DEBUG: cv2.rectangle(rejected, (x, y), (x + w, y + h), (100, 100, 100), 1) cv2.putText(rejected, str(index_), (x, y - 5), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255)) # Make a white copy of our image new_image = edges.copy() new_image.fill(255) boxes = [] # For each box, find the foreground and background intensities for index_, (contour_, box) in enumerate(keepers): # Find the average intensity of the edge pixels to # determine the foreground intensity fg_int = 0.0 for p in contour_: fg_int += ii(p[0][0], p[0][1]) fg_int /= len(contour_) if DEBUG: print "FG Intensity for #%d = %d" % (index_, fg_int) # Find the intensity of three pixels going around the # outside of each corner of the bounding box to determine # the background intensity x_, y_, width, height = box bg_int = \ [ # bottom left corner 3 pixels ii(x_ - 1, y_ - 1), ii(x_ - 1, y_), ii(x_, y_ - 1), # bottom right corner 3 pixels ii(x_ + width + 1, y_ - 1), ii(x_ + width, y_ - 1), ii(x_ + width + 1, y_), # top left corner 3 pixels ii(x_ - 1, y_ + height + 1), ii(x_ - 1, y_ + height), ii(x_, y_ + height + 1), # top right corner 3 pixels ii(x_ + width + 1, y_ + height + 1), ii(x_ + width, y_ + height + 1), ii(x_ + width + 1, y_ + height) ] # Find the median of the background # pixels determined above bg_int = np.median(bg_int) if DEBUG: print "BG Intensity for #%d = %s" % (index_, repr(bg_int)) # Determine if the box should be inverted if fg_int >= bg_int: fg = 255 bg = 0 else: fg = 0 bg = 255 # Loop through every pixel in the box and color the # pixel accordingly for x in range(x_, x_ + width): for y in range(y_, y_ + height): if y >= img_y or x >= img_x: if DEBUG: print "pixel out of bounds (%d,%d)" % (y, x) continue if ii(x, y) > fg_int: new_image[y][x] = bg else: new_image[y][x] = fg # blur a bit to improve ocr accuracy new_image = cv2.blur(new_image, (2, 2)) cv2.imwrite(output_file, new_image) if DEBUG: cv2.imwrite('edges.png', edges) cv2.imwrite('processed.png', processed) cv2.imwrite('rejected.png', rejected)