当您使用光学字符识别（OCR）或任何数据或对象识别问题时，首先要做的是预处理。这里的预处理意味着提取我们信息所在的位置。提取位置后，将对该图像执行任何机器算法。

当您必须检测位于任何表/框或行列格式的对象时，会出现问题。如果图像是这样的，那么你必须检测边框并逐个提取它们。现在应该准确地完成所有图像。作为示例，请参见以下图像：

用于提取信息的图像的示例

这里，对于该图像，我想要对所有等式进行光学字符识别。我想逐个提取每个单元格(不是任何空白)来检测这些数字。提取每个单元后，我将对所有的数字进行分割，并应用我的机器学习模型进行识别。对于这个算法，我们将使用python语言使用opencv和numpy，一个一个开始提取每个单元格:

首先导入一些库:

import cv2

import numpy as np

现在读取图像，将其转换为灰度，进行阈值处理并反转图像

# Read the image

img = cv2.imread(img_for_box_extraction_path, 0)

# Thresholding the image

(thresh, img_bin) = cv2.threshold(img, 128, 255,cv2.THRESH_BINARY| cv2.THRESH_OTSU)

# Invert the image

img_bin = 255-img_bin

cv2.imwrite("Image_bin.jpg",img_bin)

所以我们的图像看起来像这样：

现在我们需要检测边框。为此我们将使用形态学操作。为此，我们将根据图像的长度和宽度定义矩形内核（kernel ）。我们将定义两个内核。1）内核检测水平线。2）内核检测垂直线。

# Defining a kernel length

kernel_length = np.array(img).shape[1]//80

# A verticle kernel of (1 X kernel_length), which will detect all the verticle lines from the image.

verticle_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, kernel_length))

# A horizontal kernel of (kernel_length X 1), which will help to detect all the horizontal line from the image.

hori_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_length, 1))

# A kernel of (3 X 3) ones.

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))

现在在定义内核之后，我们将进行形态学操作来检测垂直和水平线。下面的代码显示包含垂直线的图像。

# Morphological operation to detect vertical lines from an image

img_temp1 = cv2.erode(img_bin, verticle_kernel, iterations=3)

verticle_lines_img = cv2.dilate(img_temp1, verticle_kernel, iterations=3)

cv2.imwrite("verticle_lines.jpg",verticle_lines_img)

# Morphological operation to detect horizontal lines from an image

img_temp2 = cv2.erode(img_bin, hori_kernel, iterations=3)

horizontal_lines_img = cv2.dilate(img_temp2, hori_kernel, iterations=3)

cv2.imwrite("horizontal_lines.jpg",horizontal_lines_img)

包含垂直线的图像

包含水平线的图像

现在我们将添加这两个图像。这将只有框，并且框中写入的信息将被删除。因此我们可以准确地检测框，并且不会出现虚假框提取的噪音。

# Weighting parameters, this will decide the quantity of an image to be added to make a new image.

alpha = 0.5

beta = 1.0 - alpha

# This function helps to add two image with specific weight parameter to get a third image as summation of two image.

img_final_bin = cv2.addWeighted(verticle_lines_img, alpha, horizontal_lines_img, beta, 0.0)

img_final_bin = cv2.erode(~img_final_bin, kernel, iterations=2)

(thresh, img_final_bin) = cv2.threshold(img_final_bin, 128,255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

cv2.imwrite("img_final_bin.jpg",img_final_bin)

最终图像仅包含框

现在我们将对这个图像应用findContours()方法。这将找到所有的边框，我们将从上到下对它们进行排序。为了对轮廓进行排序，我们将使用https://www.pyimagesearch.com/2015/04/20/sorting-contours-using-python-and-opencv/提供的函数。我们将采用自上而下的方法

# Find contours for image, which will detect all the boxes

im2, contours, hierarchy = cv2.findContours(img_final_bin, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# Sort all the contours by top to bottom.

(contours, boundingBoxes) = sort_contours(contours, method="top-to-bottom")

现在循环遍历所有轮廓，找到所有框的位置并裁剪具有矩形的零件并将其保存到一个文件夹中

idx = 0

for c in contours:

# Returns the location and width,height for every contour

x, y, w, h = cv2.boundingRect(c)

if (w > 80 and h > 20) and w > 3*h:

idx += 1

new_img = img[y:y+h, x:x+w]

cv2.imwrite(cropped_dir_path+str(idx) + '.png', new_img)

# If the box height is greater then 20, widht is >80, then only save it as a box in "cropped/" folder.

if (w > 80 and h > 20) and w > 3*h:

idx += 1

new_img = img[y:y+h, x:x+w]

cv2.imwrite(cropped_dir_path+str(idx) + '.png', new_img)

现在它完成了！检查您的文件夹，您将看到包含每个提取的框的图像。如下所示

所以现在你可以使用这个图像进一步实现。你可以通过增加更改kernel_length参数来获得非常大的图像中的良好输出。

注意：此方法适用于任何地方，用于检测从OMR表到任何Excel表的数据。该方法使用正常的形态学操作，并且它擦除了所有内部信息，因此不会出现用于错误检测边框的噪声。您可以使用以下方法作为预处理并获得良好的输出。:)

边框检测的完整Python代码在这里：

import cv2

import numpy as np

def box_extraction(img_for_box_extraction_path, cropped_dir_path):

img = cv2.imread(img_for_box_extraction_path, 0) # Read the image

(thresh, img_bin) = cv2.threshold(img, 128, 255,

cv2.THRESH_BINARY | cv2.THRESH_OTSU) # Thresholding the image

img_bin = 255-img_bin # Invert the image

cv2.imwrite("Image_bin.jpg",img_bin)

# Defining a kernel length

kernel_length = np.array(img).shape[1]//40

# A verticle kernel of (1 X kernel_length), which will detect all the verticle lines from the image.

verticle_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, kernel_length))

# A horizontal kernel of (kernel_length X 1), which will help to detect all the horizontal line from the image.

hori_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_length, 1))

# A kernel of (3 X 3) ones.

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))

# Morphological operation to detect verticle lines from an image

img_temp1 = cv2.erode(img_bin, verticle_kernel, iterations=3)

verticle_lines_img = cv2.dilate(img_temp1, verticle_kernel, iterations=3)

cv2.imwrite("verticle_lines.jpg",verticle_lines_img)

# Morphological operation to detect horizontal lines from an image

img_temp2 = cv2.erode(img_bin, hori_kernel, iterations=3)

horizontal_lines_img = cv2.dilate(img_temp2, hori_kernel, iterations=3)

cv2.imwrite("horizontal_lines.jpg",horizontal_lines_img)

# Weighting parameters, this will decide the quantity of an image to be added to make a new image.

alpha = 0.5

beta = 1.0 - alpha

# This function helps to add two image with specific weight parameter to get a third image as summation of two image.

img_final_bin = cv2.addWeighted(verticle_lines_img, alpha, horizontal_lines_img, beta, 0.0)

img_final_bin = cv2.erode(~img_final_bin, kernel, iterations=2)

(thresh, img_final_bin) = cv2.threshold(img_final_bin, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

# For Debugging

# Enable this line to see verticle and horizontal lines in the image which is used to find boxes

cv2.imwrite("img_final_bin.jpg",img_final_bin)

# Find contours for image, which will detect all the boxes

im2, contours, hierarchy = cv2.findContours(

img_final_bin, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# Sort all the contours by top to bottom.

(contours, boundingBoxes) = sort_contours(contours, method="top-to-bottom")

idx = 0

for c in contours:

# Returns the location and width,height for every contour

x, y, w, h = cv2.boundingRect(c)

# If the box height is greater then 20, widht is >80, then only save it as a box in "cropped/" folder.

if (w > 80 and h > 20) and w > 3*h:

idx += 1

new_img = img[y:y+h, x:x+w]

cv2.imwrite(cropped_dir_path+str(idx) + '.png', new_img)

box_extraction("41.jpg", "./Cropped/")