我正在使用pytessearct从图像中提取文本。但是对于倾斜的图像，它无法正常工作。请看下面的图像：

这是提取文本的代码，对于未倾斜的图像运行正常。

img = cv2.imread(<path_to_image>)gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)blur = cv2.GaussianBlur(gray, (5,5),0)ret3, thresh = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)def findSignificantContours (img, edgeImg):    contours, heirarchy = cv2.findContours(edgeImg, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)    # Find level 1 contours    level1 = []    for i, tupl in enumerate(heirarchy[0]):        # Each array is in format (Next, Prev, First child, Parent)        # Filter the ones without parent        if tupl[3] == -1:            tupl = np.insert(tupl, 0, [i])            level1.append(tupl)    significant = []    tooSmall = edgeImg.size * 5 / 100 # If contour isn't covering 5% of total area of image then it probably is too small    for tupl in level1:        contour = contours[tupl[0]];        area = cv2.contourArea(contour)        if area > tooSmall:            significant.append([contour, area])        # Draw the contour on the original image        cv2.drawContours(img, [contour], 0, (0,255,0),2, cv2.LINE_AA, maxLevel=1)    significant.sort(key=lambda x: x[1])    #print ([x[1] for x in significant]);    mx = (0,0,0,0)      # biggest bounding box so far    mx_area = 0    for cont in contours:        x,y,w,h = cv2.boundingRect(cont)        area = w*h        if area > mx_area:            mx = x,y,w,h            mx_area = area            x,y,w,h = mx  # Output to files    roi = img[y:y+h,x:x+w]    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)    blur = cv2.GaussianBlur(gray, (5,5),0)    ret3, thresh = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)    cv2_imshow(thresh)    text = pytesseract.image_to_string(roi);     print(text); print("\n"); print(pytesseract.image_to_string(thresh));    print("\n")    return [x[0] for x in significant];edgeImg_8u = np.asarray(thresh, np.uint8)# Find contourssignificant = findSignificantContours(img, edgeImg_8u)mask = thresh.copy()mask[mask > 0] = 0cv2.fillPoly(mask, significant, 255)# Invert maskmask = np.logical_not(mask)#Finally remove the backgroundimg[mask] = 0;

Tesseract无法从此图像中提取文本。有没有办法可以旋转它以完美对齐文本，然后将其输入到pytesseract中？请告诉我我的问题是否需要进一步澄清。

回答：

这里有一个简单的方法：

1. 获得二值图像。 加载图像，转换为灰度，进行高斯模糊，然后进行Otsu阈值处理。

2. 查找轮廓并按最大轮廓排序。我们查找轮廓，然后使用cv2.contourArea()通过轮廓面积进行过滤，以隔离矩形轮廓。

3. 执行透视变换。接下来，我们使用cv2.contourArea()进行轮廓近似以获得矩形轮廓。最后，我们利用imutils.perspective.four_point_transform实际获得图像的鸟瞰图。

二值图像

结果

要实际提取文本，请查看

1. 使用pytesseract OCR从图像中识别文本

2. 清理图像以进行OCR

3. 使用Python和OpenCV检测图像中的文本区域

代码

from imutils.perspective import four_point_transformimport cv2import numpy# Load image, grayscale, Gaussian blur, Otsu's thresholdimage = cv2.imread("1.jpg")gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)blur = cv2.GaussianBlur(gray, (7,7), 0)thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]# Find contours and sort for largest contourcnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)cnts = cnts[0] if len(cnts) == 2 else cnts[1]cnts = sorted(cnts, key=cv2.contourArea, reverse=True)displayCnt = Nonefor c in cnts:    # Perform contour approximation    peri = cv2.arcLength(c, True)    approx = cv2.approxPolyDP(c, 0.02 * peri, True)    if len(approx) == 4:        displayCnt = approx        break# Obtain birds' eye view of imagewarped = four_point_transform(image, displayCnt.reshape(4, 2))cv2.imshow("thresh", thresh)cv2.imshow("warped", warped)cv2.waitKey()