如何利用图像预处理提高OCR的准确性？-技术圈

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重磅干货，第一时间送达

OCR代表光学字符识别，将文档照片或场景照片转换为机器编码的文本。有很多工具可以在你们的系统中实现OCR，例如Tesseract OCR和Cloud Vision。他们使用AI和机器学习以及经过训练的自定义模型。文本识别取决于多种因素，以产生高质量的输出。OCR输出在很大程度上取决于输入图像的质量，这就是每个OCR引擎都提供有关输入图像质量及其大小的准则的原因，这些准则可帮助OCR引擎产生准确的结果。

图像预处理功能可以提高输入图像的质量，以便OCR引擎为我们提供准确的输出，使用以下图像处理操作可以改善输入图像的质量。

图像缩放

图像缩放比例对于图像分析很重要。通常，OCR引擎会准确输出300 DPI的图像。DPI描述了图像的分辨率，换句话说，它表示每英寸的打印点数。

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

偏斜矫正

歪斜图像定义为不直的文档图像。歪斜的图像会直接影响OCR引擎的行分割，从而降低其准确性。我们需要执行以下步骤来更正文本倾斜。

1.检测图像中歪斜的文本块

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 = Nonefor 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.计算旋转角度
3.旋转图像以校正歪斜

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

二值化

通常，OCR引擎会在内部进行二值化处理，因为它们可以处理黑白图像。最简单的方法是计算阈值，然后将所有像素转换为白色，且其值高于阈值，其余像素转换为黑色。

除噪或降噪

噪点是图像像素之间颜色或亮度的随机变化。噪声会降低图像中文本的可读性。噪声有两种主要类型：盐椒噪声和高斯噪声。

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

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