Detect image orientation angle based on text direction

Question

I am working on a OCR task to extract information from multiple ID proof documents. One challenge is the orientation of the scanned image. The need is to fix the orientation

Answer 1

Here's an approach based on the assumption that the majority of the text is skewed onto one side. The idea is that we can determine the angle based on the where the major text region is located

• Convert image to grayscale and Gaussian blur
• Adaptive threshold to get a binary image
• Find contours and filter using contour area
• Draw filtered contours onto mask
• Split image horizontally or vertically based on orientation
• Count number of pixels in each half

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After converting to grayscale and Gaussian blurring, we adaptive threshold to obtain a binary image

From here we find contours and filter using contour area to remove the small noise particles and the large border. We draw any contours that pass this filter onto a mask

To determine the angle, we split the image in half based on the image's dimension. If width > height then it must be a horizontal image so we split in half vertically. if height > width then it must be a vertical image so we split in half horizontally

Now that we have two halves, we can use cv2.countNonZero() to determine the amount of white pixels on each half. Here's the logic to determine angle:

if horizontal
    if left >= right 
        degree -> 0
    else 
        degree -> 180
if vertical
    if top >= bottom
        degree -> 270
    else
        degree -> 90

left 9703

right 3975

Therefore the image is 0 degrees. Here's the results from other orientations

left 3975

right 9703

We can conclude that the image is flipped 180 degrees

Here's results for vertical image. Note since its a vertical image, we split horizontally

top 3947

bottom 9550

Therefore the result is 90 degrees

import cv2
import numpy as np

def detect_angle(image):
    mask = np.zeros(image.shape, dtype=np.uint8)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (3,3), 0)
    adaptive = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,15,4)

    cnts = cv2.findContours(adaptive, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if len(cnts) == 2 else cnts[1]

    for c in cnts:
        area = cv2.contourArea(c)
        if area < 45000 and area > 20:
            cv2.drawContours(mask, [c], -1, (255,255,255), -1)

    mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
    h, w = mask.shape
    
    # Horizontal
    if w > h:
        left = mask[0:h, 0:0+w//2]
        right = mask[0:h, w//2:]
        left_pixels = cv2.countNonZero(left)
        right_pixels = cv2.countNonZero(right)
        return 0 if left_pixels >= right_pixels else 180
    # Vertical
    else:
        top = mask[0:h//2, 0:w]
        bottom = mask[h//2:, 0:w]
        top_pixels = cv2.countNonZero(top)
        bottom_pixels = cv2.countNonZero(bottom)
        return 90 if bottom_pixels >= top_pixels else 270

if __name__ == '__main__':
    image = cv2.imread('1.png')
    angle = detect_angle(image)
    print(angle)