CODEWITHSUNDEEP
pythonopencvimage-processingtext-recognition
S. Hersister
S. Hersister

Reputation: 51

Filtering Image For Improving Text Recognition

I have this source image below (after cropped) and I try to do some image processing before I read text.

Image1

With python and opencv, I tried to remove the lines in the background with k-means with k =2, and the result is

Image2

I tried to smooth the image using this code below

def process_image_for_ocr(file_path):
# TODO : Implement using opencv
temp_filename = set_image_dpi(file_path)
im_new = remove_noise_and_smooth(temp_filename)
return im_new


def set_image_dpi(file_path):
    im = Image.open(file_path)
    length_x, width_y = im.size
    factor = max(1, int(IMAGE_SIZE / length_x))
    size = factor * length_x, factor * width_y
    # size = (1800, 1800)
    im_resized = im.resize(size, Image.ANTIALIAS)
    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.jpg')
    temp_filename = temp_file.name
    im_resized.save(temp_filename, dpi=(300, 300))
    return temp_filename


def image_smoothening(img):
    ret1, th1 = cv2.threshold(img, BINARY_THREHOLD, 255, cv2.THRESH_BINARY)
    ret2, th2 = cv2.threshold(th1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    blur = cv2.GaussianBlur(th2, (1, 1), 0)
    ret3, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    return th3


def remove_noise_and_smooth(file_name):
    img = cv2.imread(file_name, 0)
    filtered = cv2.adaptiveThreshold(img.astype(np.uint8), 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 41, 3)
    kernel = np.ones((1, 1), np.uint8)
    opening = cv2.morphologyEx(filtered, cv2.MORPH_OPEN, kernel)
    closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
    img = image_smoothening(img)
    or_image = cv2.bitwise_or(img, closing)
    return or_image

And the result is

Image3

Can you help me (any idea) to remove the lines on the background of the source image?

Upvotes: 4

Views: 3046

Answers (1)

D.Griffiths
D.Griffiths

Reputation: 2307

One approach to achieve this is by computing a k-means unsupervised segmentation of the image. You just need to play with the k and i_val values to get the desired output.

First, you need to create a function which will find the k threshold values.This simply calculates an image histogram which is used to compute the k_means. .ravel() just converts your numpy array to a 1-D array. np.reshape(img, (-1,1)) then converts it to an 2-D array which is of shape n,1. Next we carry out the k_means as described here.

The function takes the input gray-scale image, your number of k intervals and the value you want to threshold from (i_val). It returns the threshold value at your desired i_val. 

def kmeans(input_img, k, i_val):
    hist = cv2.calcHist([input_img],[0],None,[256],[0,256])
    img = input_img.ravel()
    img = np.reshape(img, (-1, 1))
    img = img.astype(np.float32)

    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
    flags = cv2.KMEANS_RANDOM_CENTERS
    compactness,labels,centers = cv2.kmeans(img,k,None,criteria,10,flags)
    centers = np.sort(centers, axis=0)

    return centers[i_val].astype(int), centers, hist

img = cv2.imread('Y8CSE.jpg', 0)
_, thresh = cv2.threshold(img, kmeans(input_img=img, k=8, i_val=2)[0], 255, cv2.THRESH_BINARY)
cv2.imwrite('text.png',thresh)

The output for this looks like:

K-MEANS threshold

You could carry on with this method by using morphological operators, or pre-mask the image using a hough transform as seen in the first answer here.

Upvotes: 4

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