i tried getting individual characters from the image and passing them through the ocr, but the result is jumbled up characters. Passing the whole image is at least returning the characters in order but it seems like the ocr is trying to read all the other contours as well.
example image: Image being used
The result : 6A7J7B0
Desired result : AJB6779
The code
img = cv2.imread("data/images/car6.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# resize image to three times as large as original for better readability
gray = cv2.resize(gray, None, fx = 3, fy = 3, interpolation = cv2.INTER_CUBIC)
# perform gaussian blur to smoothen image
blur = cv2.GaussianBlur(gray, (5,5), 0)
# threshold the image using Otsus method to preprocess for tesseract
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV)
# create rectangular kernel for dilation
rect_kern = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
# apply dilation to make regions more clear
dilation = cv2.dilate(thresh, rect_kern, iterations = 1)
# find contours of regions of interest within license plate
try:
contours, hierarchy = cv2.findContours(dilation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
except:
ret_img, contours, hierarchy = cv2.findContours(dilation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# sort contours left-to-right
sorted_contours = sorted(contours, key=lambda ctr: cv2.boundingRect(ctr)[0])
# create copy of gray image
im2 = gray.copy()
# create blank string to hold license plate number
plate_num = ""
# loop through contours and find individual letters and numbers in license plate
for cnt in sorted_contours:
x,y,w,h = cv2.boundingRect(cnt)
height, width = im2.shape
# if height of box is not tall enough relative to total height then skip
if height / float(h) > 6: continue
ratio = h / float(w)
# if height to width ratio is less than 1.5 skip
if ratio < 1.5: continue
# if width is not wide enough relative to total width then skip
if width / float(w) > 15: continue
area = h * w
# if area is less than 100 pixels skip
if area < 100: continue
# draw the rectangle
rect = cv2.rectangle(im2, (x,y), (x+w, y+h), (0,255,0),2)
# grab character region of image
roi = thresh[y-5:y+h+5, x-5:x+w+5]
# perfrom bitwise not to flip image to black text on white background
roi = cv2.bitwise_not(roi)
# perform another blur on character region
roi = cv2.medianBlur(roi, 5)
try:
text = pytesseract.image_to_string(roi, config='-c tessedit_char_whitelist=0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ --psm 8 --oem 3')
# clean tesseract text by removing any unwanted blank spaces
clean_text = re.sub('[\W_]+', '', text)
plate_num += clean_text
except:
text = None
if plate_num != None:
print("License Plate #: ", plate_num)
