view.py

import cv2
import sys
import pytesseract
import numpy as np
from matplotlib import pyplot as plt
#import matplotlib.pyplot as plt

#
# /usr/share/tesseract-ocr/4.00/tessdata
#

def ImageToText(string,img):

#   psm
#   0 = Orientation and script detection (OSD) only.
#   1 = Automatic page segmentation with OSD.
#   2 = Automatic page segmentation, but no OSD, or OCR. (not implemented)
#   3 = Fully automatic page segmentation, but no OSD. (Default)
#   4 = Assume a single column of text of variable sizes.
#   5 = Assume a single uniform block of vertically aligned text.
#   6 = Assume a single uniform block of text.
#   7 = Treat the image as a single text line.
#   8 = Treat the image as a single word.
#   9 = Treat the image as a single word in a circle.
#   10 = Treat the image as a single character.
#   11 = Sparse text. Find as much text as possible in no particular order.
#   12 = Sparse text with OSD.
#   13 = Raw line. Treat the image as a single text line,
#        bypassing hacks that are Tesseract-specific.
   config = ('-l eng+kor  --psm 6')
   text = pytesseract.image_to_string(img,config=config)   
   print"------start --------(%s)" %(string)
   print(text)
   print"------end ---------\n"
   return text

if __name__ == '__main__':

   if len(sys.argv) < 2 :
      print('Usage : python ocr_simple.py image.jpg')
      sys.exit(1)

   imPath = sys.argv[1]
   print("Open CV Version"+ cv2.__version__)

   img = cv2.imread(imPath,cv2.IMREAD_GRAYSCALE)

   #Global Thresholding (v = 127)
   ret,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)

   #Adaptive Mean Thresholding
   th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2)
   
   #Adaptive Gaussian Thresholding
   th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
   
   #others 
   #blur = cv2.GaussianBlur(img,(5,5),0)
   #ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
  
   #th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2)
   #th3 = cv2.Laplacian(th3,cv2.CV_64F)


   titles = ['Original Image', 'Global Thresholding (v = 127)',
            'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
   images = [img, th1, th2, th3]

   for i in xrange(4):
       data = ImageToText(titles[i],images[i])
       plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
       plt.title(titles[i])
       plt.xlabel(data)
       plt.xticks([]),plt.yticks([])
   
   plt.show()

 #  ImageToText(titles[0],img)
 #  ImageToText(titles[1],th1)
 #  ImageToText(titles[2],th2)
 #  ImageToText(titles[3],th3)