Merge pull request #1 from jeremyholtzman/adding_work

ocr file, word doc and image, updated readme

README.md

@@ -1 +1,13 @@
-# image_to_text
+# image to text
+
+The ocr_clean.py file takes an image and uses pytesseract's image_to_string function to
+turn the image into text.
+
+Written in python2.
+The example image is word_doc_for_image.png and it's a screenshot of word_doc_for_image.doc
+
+To run the ocr file from the terminal, run the following command:
+python ocr_clean.py -i word_doc_for_image.png
+
+To run the ocr file after blurring it, run:
+python ocr_clean.py -i word_doc_for_image.png -p "blur"

ocr_clean.py

@@ -0,0 +1,58 @@
+# https://www.pyimagesearch.com/2017/07/10/using-tesseract-ocr-python/
+
+# run the file with
+# python2 ocr.py -i example_01.png
+# or
+# python2 ocr.py -i example_01.png --p blur
+
+# import the necessary packages
+# Image  class is required so that we can load our input image from disk in PIL format
+from PIL import Image
+import pytesseract
+import argparse
+import cv2
+import os
+
+# construct the argument parse and parse the arguments
+ap = argparse.ArgumentParser()
+# image
+ap.add_argument("-i", "--image", required=True, help="path to input image to be OCR")
+# preprocess : The preprocessing method. This switch is optional and for this tutorial and can accept two values:  thresh  (threshold) or blur
+ap.add_argument("-p", "--preprocess", type=str, default="thresh", help="type of preprocessing to be done")
+
+args = vars(ap.parse_args())
+
+# load the example image and convert it to grayscale
+image = cv2.imread(args["image"])
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+
+# check to see if we should apply thresholding to preprocess the
+# Next, depending on the pre-processing method specified by our command line argument,
+# we will either threshold or blur the image. This is where you would want to add more
+# advanced pre-processing methods (depending on your specific application of OCR) which are beyond the scope of this blog post.
+# image
+if args["preprocess"] == "thresh":
+	gray = cv2.threshold(gray, 0, 255,
+		cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
+# make a check to see if median blurring should be done to remove
+# noise
+elif args["preprocess"] == "blur":
+	gray = cv2.medianBlur(gray, 3)
+
+# write the grayscale image to disk as a temporary file so we can
+# apply OCR to it
+filename = "{}.png".format(os.getpid())
+cv2.imwrite(filename, gray)
+
+# load the image as a PIL/Pillow image, apply OCR, and then delete
+# the temporary file
+text = pytesseract.image_to_string(Image.open(filename))
+print text
+os.remove(filename)
+
+
+# show the output images
+cv2.imshow("Image", image)
+cv2.imshow("Output", gray)
+cv2.waitKey(0)