此答案适用于遇到带有图像的 pdf 并需要使用 OCR 的任何人。我找不到可行的现成解决方案；没有什么能给我所需的准确性。
以下是我发现可行的步骤。
使用https://poppler.freedesktop.org/ 中的pdfimages 将pdf 页面转换为图像。
使用Tesseract 检测旋转并使用ImageMagick mogrify 修复它。
使用 OpenCV 查找和提取表。
使用 OpenCV 从表格中查找和提取每个单元格。
使用 OpenCV 裁剪和清理每个单元格，这样就不会有干扰 OCR 软件的噪音。
使用 Tesseract 对每个单元格进行 OCR。
将每个单元格的提取文本组合成您需要的格式。
我编写了一个 python 包，其中包含可以帮助完成这些步骤的模块。
回购：https://github.com/eihli/image-table-ocr
文档和来源：https://eihli.github.io/image-table-ocr/pdf_table_extraction_and_ocr.html
有些步骤不需要代码，它们利用了pdfimages 和tesseract 等外部工具。我将为需要代码的几个步骤提供一些简短的示例。
- 查找表：
在了解如何查找表格时，此链接是一个很好的参考。 https://answers.opencv.org/question/63847/how-to-extract-tables-from-an-image/
import cv2
def find_tables(image):
BLUR_KERNEL_SIZE = (17, 17)
STD_DEV_X_DIRECTION = 0
STD_DEV_Y_DIRECTION = 0
blurred = cv2.GaussianBlur(image, BLUR_KERNEL_SIZE, STD_DEV_X_DIRECTION, STD_DEV_Y_DIRECTION)
MAX_COLOR_VAL = 255
BLOCK_SIZE = 15
SUBTRACT_FROM_MEAN = -2
img_bin = cv2.adaptiveThreshold(
~blurred,
MAX_COLOR_VAL,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY,
BLOCK_SIZE,
SUBTRACT_FROM_MEAN,
)
vertical = horizontal = img_bin.copy()
SCALE = 5
image_width, image_height = horizontal.shape
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (int(image_width / SCALE), 1))
horizontally_opened = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, horizontal_kernel)
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, int(image_height / SCALE)))
vertically_opened = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, vertical_kernel)
horizontally_dilated = cv2.dilate(horizontally_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (40, 1)))
vertically_dilated = cv2.dilate(vertically_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (1, 60)))
mask = horizontally_dilated + vertically_dilated
contours, hierarchy = cv2.findContours(
mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE,
)
MIN_TABLE_AREA = 1e5
contours = [c for c in contours if cv2.contourArea(c) > MIN_TABLE_AREA]
perimeter_lengths = [cv2.arcLength(c, True) for c in contours]
epsilons = [0.1 * p for p in perimeter_lengths]
approx_polys = [cv2.approxPolyDP(c, e, True) for c, e in zip(contours, epsilons)]
bounding_rects = [cv2.boundingRect(a) for a in approx_polys]
# The link where a lot of this code was borrowed from recommends an
# additional step to check the number of "joints" inside this bounding rectangle.
# A table should have a lot of intersections. We might have a rectangular image
# here though which would only have 4 intersections, 1 at each corner.
# Leaving that step as a future TODO if it is ever necessary.
images = [image[y:y+h, x:x+w] for x, y, w, h in bounding_rects]
return images
- 从表格中提取单元格。
这与2非常相似，所以我不会包含所有代码。我将参考的部分是对单元格进行排序。
我们要从左到右、从上到下识别单元格。
我们会找到最左上角的矩形。然后我们将找到所有中心位于左上角矩形的 top-y 和 bottom-y 值内的矩形。然后我们将根据它们中心的 x 值对这些矩形进行排序。我们将从列表中删除这些矩形并重复。
def cell_in_same_row(c1, c2):
c1_center = c1[1] + c1[3] - c1[3] / 2
c2_bottom = c2[1] + c2[3]
c2_top = c2[1]
return c2_top < c1_center < c2_bottom
orig_cells = [c for c in cells]
rows = []
while cells:
first = cells[0]
rest = cells[1:]
cells_in_same_row = sorted(
[
c for c in rest
if cell_in_same_row(c, first)
],
key=lambda c: c[0]
)
row_cells = sorted([first] + cells_in_same_row, key=lambda c: c[0])
rows.append(row_cells)
cells = [
c for c in rest
if not cell_in_same_row(c, first)
]
# Sort rows by average height of their center.
def avg_height_of_center(row):
centers = [y + h - h / 2 for x, y, w, h in row]
return sum(centers) / len(centers)
rows.sort(key=avg_height_of_center)