如何使用Python从PDF中将表格提取为文本？

Question

我有一个包含表格、文本和一些图像的PDF。我想在PDF中的任何位置提取表

现在我正在手动从页面中查找表。从那里，我捕获该页面并保存到另一个PDF

import PyPDF2

PDFfilename = "Sammamish.pdf" #filename of your PDF/directory where your PDF is stored

pfr = PyPDF2.PdfFileReader(open(PDFfilename, "rb")) #PdfFileReader object

pg4 = pfr.getPage(126) #extract pg 127

writer = PyPDF2.PdfFileWriter() #create PdfFileWriter object
#add pages
writer.addPage(pg4)

NewPDFfilename = "allTables.pdf" #filename of your PDF/directory where you want your new PDF to be
with open(NewPDFfilename, "wb") as outputStream:
    writer.write(outputStream) #write pages to new PDF

我的目标是从整个PDF文档中提取表

Answer 1

如果您的pdf是基于文本的，而不是扫描的文档（即，如果您可以在pdf查看器中单击并拖动以选择表格中的文本），则您可以将模块^{}用于

import camelot
tables = camelot.read_pdf('foo.pdf')

然后，您可以选择如何保存表（作为csv、json、excel、html、sqlite），以及是否应在ZIP存档中压缩输出

tables.export('foo.csv', f='csv', compress=False)

---

编辑：^{}的显示速度大约是camelot-py的6倍，因此应该改用它

import camelot
import cProfile
import pstats
import tabula

cmd_tabula = "tabula.read_pdf('table.pdf', pages='1', lattice=True)"
prof_tabula = cProfile.Profile().run(cmd_tabula)
time_tabula = pstats.Stats(prof_tabula).total_tt

cmd_camelot = "camelot.read_pdf('table.pdf', pages='1', flavor='lattice')"
prof_camelot = cProfile.Profile().run(cmd_camelot)
time_camelot = pstats.Stats(prof_camelot).total_tt

print(time_tabula, time_camelot, time_camelot/time_tabula)

给予

1.8495559890000015 11.057014036000016 5.978199147125147

Answer 2

• 我建议您使用tabla提取表格
• 将pdf作为参数传递给table api，它将以dataframe的形式返回表
• pdf中的每个表都作为一个数据帧返回
• 该表将在dataframea列表中返回，用于处理所需的dataframe

这是我提取pdf的代码

import pandas as pd
import tabula
file = "filename.pdf"
path = 'enter your directory path here'  + file
df = tabula.read_pdf(path, pages = '1', multiple_tables = True)
print(df)

有关更多详情，请参阅我的repo

Answer 3

这个答案适用于任何遇到带有图像的PDF并需要使用OCR的人。我找不到可行的现成解决方案；没有什么能给我提供我所需要的准确度

以下是我发现有效的步骤

1. 使用https://poppler.freedesktop.org/中的pdfimages将pdf页面转换为图像

2. 使用Tesseract检测旋转，使用ImageMagick{}修复旋转

3. 使用OpenCV查找和提取表

4. 使用OpenCV查找并从表中提取每个单元格

5. 使用OpenCV对每个单元格进行裁剪和清理，这样就不会有干扰OCR软件的噪音

6. 使用Tesseract对每个单元格进行OCR

7. 将每个单元格的提取文本合并为所需的格式

我编写了一个python包，其中的模块可以帮助完成这些步骤

回购：https://github.com/eihli/image-table-ocr

文件及；资料来源：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。我将为确实需要代码的两个步骤提供一些简短的示例

1. 查找表：

在了解如何查找表时，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

1. 从表中提取单元格

这与2非常相似，因此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

我们想从左到右，从上到下识别细胞

我们将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上y和下y值范围内的矩形。然后我们将根据矩形中心的x值对其进行排序。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)