如何利用圖像預先處理SOP 藉此提高OCR的準確性？

資料來源 : https://mp.weixin.qq.com/s/J6Vm6ivm2jUYD20vDiLwbg

01.圖像縮放

def set_image_dpi(file_path):
    im = Image.open(file_path)
    length_x, width_y = im.size
    factor = min(1, float(1024.0 / length_x))
    size = int(factor * length_x), int(factor * width_y)
    im_resized = im.resize(size, Image.ANTIALIAS)
    temp_file = tempfile.NamedTemporaryFile(delete=False,   suffix='.png')
    temp_filename = temp_file.name
    im_resized.save(temp_filename, dpi=(300, 300))
    return temp_filenam

02.偏斜矯正

	1.檢測圖像中歪斜的文本塊
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	gray = cv2.GaussianBlur(gray, (5, 5), 0)
	edged = cv2.Canny(gray, 10, 50)
	cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
	cnts = cnts[0] if imutils.is_cv2() else cnts[1]
	cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
	screenCnt = None
	for c in cnts:
		peri = cv2.arcLength(c, True)
		approx = cv2.approxPolyDP(c, 0.02 * peri, True)
		if len(approx) == 4:
			screenCnt = approx
			break

	cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)

	2.計算旋轉角度
	3.旋轉圖像以校正歪斜
	pts = np.array(screenCnt.reshape(4, 2) * ratio)
	warped = four_point_transform(orig, pts)
	def order_points(pts):
		# initialzie a list of coordinates that will be ordered
		# such that the first entry in the list is the top-left,
		# the second entry is the top-right, the third is the
		# bottom-right, and the fourth is the bottom-left
		rect = np.zeros((4, 2), dtype="float32")

		# the top-left point will have the smallest sum, whereas
		# the bottom-right point will have the largest sum
		s = pts.sum(axis=1)
		rect[0] = pts[np.argmin(s)]
		rect[2] = pts[np.argmax(s)]

		# now, compute the difference between the points, the
		# top-right point will have the smallest difference,
		# whereas the bottom-left will have the largest difference
		diff = np.diff(pts, axis=1)
		rect[1] = pts[np.argmin(diff)]
		rect[3] = pts[np.argmax(diff)]

		# return the ordered coordinates
		return rect


	def four_point_transform(image, pts):
		# obtain a consistent order of the points and unpack them
		# individually
		rect = order_points(pts)
		(tl, tr, br, bl) = rect

		# compute the width of the new image, which will be the
		# maximum distance between bottom-right and bottom-left
		# x-coordiates or the top-right and top-left x-coordinates
		widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
		widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
		maxWidth = max(int(widthA), int(widthB))

		# compute the height of the new image, which will be the
		# maximum distance between the top-right and bottom-right
		# y-coordinates or the top-left and bottom-left y-coordinates
		heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
		heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
		maxHeight = max(int(heightA), int(heightB))

		# now that we have the dimensions of the new image, construct
		# the set of destination points to obtain a "birds eye view",
		# (i.e. top-down view) of the image, again specifying points
		# in the top-left, top-right, bottom-right, and bottom-left
		# order
		dst = np.array([
			[0, 0],
			[maxWidth - 1, 0],
			[maxWidth - 1, maxHeight - 1],
			[0, maxHeight - 1]], dtype="float32")

		# compute the perspective transform matrix and then apply it
		M = cv2.getPerspectiveTransform(rect, dst)
		warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
		return warped

03.二值化&除噪或降噪

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, 9, 41)
    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