要对页面上的元素进行分组，比如文本和图像的段落，可以使用一些聚类算法，和/或带有一些阈值的斑点检测。
您可以使用Radon变换来识别线条并检测扫描页面的倾斜。
我认为，字符分离你将不得不搞乱字体。一些多项式匹配/拟合或什么的。（这是一个非常疯狂的猜测，现在，不要把它当真）。但类似的方法将允许你得到的字符线，并识别它在同一步骤。
至于识别，一旦你有了一个字符，有一个很好的三角学技巧来比较字符的Angular 和数据库中存储的Angular 。
我不是一个Maven如何页面分割确切的工作，但它似乎是我的方式成为一个。只是在一个项目的工作，包括它。所以给予我一个月，我将能够告诉你更多。：D
无论如何，你应该去阅读Tesseract代码，看看惠普和谷歌是如何做到的。它应该会给予你很好的想法。
祝你好运！

在完成对象检测后，您可以执行文本检测，并将其传递给Tesseract。在将图像传递给检测器函数之前，可以有多种变化来增强图像。
参考文件https://arxiv.org/abs/1704.03155v2https://arxiv.org/pdf/2002.07662.pdf

def text_detector(image):
#hasFrame, image = cap.read()
orig = image
(H, W) = image.shape[:2]

(newW, newH) = (640, 320)
rW = W / float(newW)
rH = H / float(newH)

image = cv2.resize(image, (newW, newH))
(H, W) = image.shape[:2]

layerNames = [
    "feature_fusion/Conv_7/Sigmoid",
    "feature_fusion/concat_3"]

blob = cv2.dnn.blobFromImage(image, 1.0, (W, H),
    (123.68, 116.78, 103.94), swapRB=True, crop=False)

net.setInput(blob)
(scores, geometry) = net.forward(layerNames)

(numRows, numCols) = scores.shape[2:4]
rects = []
confidences = []

for y in range(0, numRows):

    scoresData = scores[0, 0, y]
    xData0 = geometry[0, 0, y]
    xData1 = geometry[0, 1, y]
    xData2 = geometry[0, 2, y]
    xData3 = geometry[0, 3, y]
    anglesData = geometry[0, 4, y]

    # loop over the number of columns
    for x in range(0, numCols):
        # if our score does not have sufficient probability, ignore it
        if scoresData[x] < 0.5:
            continue

        # compute the offset factor as our resulting feature maps will
        # be 4x smaller than the input image
        (offsetX, offsetY) = (x * 4.0, y * 4.0)

        # extract the rotation angle for the prediction and then
        # compute the sin and cosine
        angle = anglesData[x]
        cos = np.cos(angle)
        sin = np.sin(angle)

        # use the geometry volume to derive the width and height of
        # the bounding box
        h = xData0[x] + xData2[x]
        w = xData1[x] + xData3[x]

        # compute both the starting and ending (x, y)-coordinates for
        # the text prediction bounding box
        endX = int(offsetX + (cos * xData1[x]) + (sin * xData2[x]))
        endY = int(offsetY - (sin * xData1[x]) + (cos * xData2[x]))
        startX = int(endX - w)
        startY = int(endY - h)

        # add the bounding box coordinates and probability score to
        # our respective lists
        rects.append((startX, startY, endX, endY))
        confidences.append(scoresData[x])

boxes = non_max_suppression(np.array(rects), probs=confidences)

for (startX, startY, endX, endY) in boxes:

    startX = int(startX * rW)
    startY = int(startY * rH)
    endX = int(endX * rW)
    endY = int(endY * rH)

    # draw the bounding box on the image
    cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 255, 0), 3)
return orig