title: How to tune tesseract for identifying number plate of a car more accurately?
body: <p>I have a code to detect and identify the car number plate and convert the image into text using tesseract.
  I am using openCV to localise the number plate.
  The problem that I am facing is that tesseract is not accurately identifying the number. Is there any way I can improve the tesseract performance?</p>
  
  <p>My code (which I downloaded from Internet) is:</p>
  
  <pre><code>import numpy as np
  import cv2
  # from copy import deepcopy
  from PIL import Image
  import pytesseract as tess
  
  # plate = 0
  def preprocess(img):
      # print ('preprocessing image')
      # cv2.imshow("Input", img)
      imgBlurred = cv2.GaussianBlur(img, (5, 5), 0)
      gray = cv2.cvtColor(imgBlurred, cv2.COLOR_BGR2GRAY)
  
      sobelx = cv2.Sobel(gray, cv2.CV_8U, 1, 0, ksize=3)
      cv2.imshow("Sobel",sobelx)
      cv2.waitKey(0)
      ret2, threshold_img = cv2.threshold(sobelx, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
      cv2.imshow("Threshold",threshold_img)
      cv2.waitKey(0)
      return threshold_img
  
  
  def cleanPlate(plate):
      # print ("CLEANING PLATE. . .")
      gray = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)
      kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
      thresh= cv2.dilate(gray, kernel, iterations=1)
  
      _, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)
      im1, contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
  
      if contours:
          areas = [cv2.contourArea(c) for c in contours]
          max_index = np.argmax(areas)
  
          max_cnt = contours[max_index]
          max_cntArea = areas[max_index]
          x, y, w, h = cv2.boundingRect(max_cnt)
  
          if not ratioCheck(max_cntArea, w, h):
              return plate, None
  
          cleaned_final = thresh[y:y + h, x:x + w]
          # cv2.imshow("Function Test",cleaned_final)
          return cleaned_final, [x, y, w, h]
  
      else:
          return plate, None
  
  
  def extract_contours(threshold_img):
      # print ('extracting contours')
      element = cv2.getStructuringElement(shape=cv2.MORPH_RECT, ksize=(17, 3))
      morph_img_threshold = threshold_img.copy()
      cv2.morphologyEx(src=threshold_img, op=cv2.MORPH_CLOSE, kernel=element, dst=morph_img_threshold)
      cv2.imshow("Morphed", morph_img_threshold)
      cv2.waitKey(0)
  
      im2, contours, hierarchy = cv2.findContours(morph_img_threshold, mode=cv2.RETR_EXTERNAL,
                                                  method=cv2.CHAIN_APPROX_NONE)
      return contours
  
  
  def ratioCheck(area, width, height):
      # print ('checking ratio')
      ratio = float(width) / float(height)
      if ratio &lt; 1:
          ratio = 1 / ratio
  
      aspect = 4.7272
      min = 15 * aspect * 15  # minimum area
      max = 125 * aspect * 125  # maximum area
  
      rmin = 3
      rmax = 6
  
      if (area &lt; min or area &gt; max) or (ratio &lt; rmin or ratio &gt; rmax):
          return False
      return True
  
  
  def isMaxWhite(plate):
      # print ('is Max white')
      avg = np.mean(plate)
      if (avg &gt;= 115):
          return True
      else:
          return False
  
  
  def validateRotationAndRatio(rect):
      # print( 'validate the rotation and ratio')
      (x, y), (width, height), rect_angle = rect
  
      if (width &gt; height):
          angle = -rect_angle
      else:
          angle = 90 + rect_angle
  
      if angle &gt; 15:
          return False
  
      if height == 0 or width == 0:
          return False
  
      area = height * width
      if not ratioCheck(area, width, height):
          return False
      else:
          return True
  
  
  def cleanAndRead(img, contours):
      # print ('clean and read')
      # count=0
      for i, cnt in enumerate(contours):
          min_rect = cv2.minAreaRect(cnt)
  
          if validateRotationAndRatio(min_rect):
  
              x, y, w, h = cv2.boundingRect(cnt)
              plate_img = img[y:y + h, x:x + w]
  
              if (isMaxWhite(plate_img)):
                  # count+=1
                  clean_plate, rect = cleanPlate(plate_img)
  
                  if rect:
                      x1, y1, w1, h1 = rect
                      x, y, w, h = x + x1, y + y1, w1, h1
                      cv2.imshow("Cleaned Plate", clean_plate)
                      cv2.waitKey(0)
                      plate_im = Image.fromarray(clean_plate)
                      plate_im.save('donald1.png')
                      text = tess.image_to_string(plate_im, lang='eng')
                      # print text
                      img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
  
                      cv2.imshow("Detected Plate", img)
                      cv2.waitKey(0)
                      return text
  
  
  
  
  
  numberplate = 0
  
  
  img = cv2.imread("car_number_plate.jpg")
  
  threshold_img = preprocess(img)
  
  contours = extract_contours(threshold_img)
  
      # if len(contours)!=0:
      # print len(contours) #Test
      # cv2.drawContours(img, contours, -1, (0,255,0), 1)
      # cv2.imshow("Contours",img)
      # cv2.waitKey(0)
  
  
  plate = cleanAndRead(img, contours)
  print ('plate information: ', plate)
  </code></pre>
  
  <p>If my number plate is: MH01AV8866</p>
  
  <p>It will be recognised as MH01AY8866</p>
  
  <p>Any suggestion will be appreciated. Let me know if any other information is required too.</p>