python - 如何使用 python 从 pdf 文件中使用 camelot 提取表名和表？

Question

我正在尝试使用 python 中的 camelot 从 pdf 文件中提取表格和表格名称。虽然我知道如何使用 camelot 提取表(这非常简单)，但我正在努力寻找有关如何提取表名的任何帮助。目的是提取此信息并显示表及其名称的可视化，以便用户从列表中选择相关表。

我试过提取表格，然后从 pdf 中提取文本。我在这两个方面都成功了，但在将表名连接到表方面却没有成功。

def tables_from_pdfs(filespath):
    pdffiles = glob.glob(os.path.join(filespath, "*.pdf"))
    print(pdffiles)
    dictionary = {}
    keys = []
    for file in pdffiles:
        print(file)
        n = PyPDF2.PdfFileReader(open(file, 'rb')).getNumPages()
        print(n)
        tables_dict = {}
        for i in range(n):
            tables = camelot.read_pdf(file, pages = str(i))
            tables_dict[i] = tables
        head, tail = os.path.split(file)
        tail = tail.replace(".pdf", "")
        keys.append(tail)
        dictionary[tail] = tables_dict
    return dictionary, keys

预期的结果是一个表格和表格的名称，如 pdf 文件中所述。例如:pdf 名称第 x 页上的表:表 1. Blah Blah blah'''表格'''

Answer 1

我找到了一个相对的解决方案。至少对我有用。

import os, PyPDF2, time, re, shutil
import pytesseract
from pdf2image import convert_from_path
import camelot
import datefinder
from difflib import SequenceMatcher

def similar(a, b):
    return SequenceMatcher(None, a, b).ratio()

similarityAmt = 0.6 # find with 60% similarity
def find_table_name(dataframe, documentString):
    
    # Assuming that you extracted the text from a PDF, it should be multi-lined. We split by line
    stringsSeparated = text.split("\n")
    for i, string in enumerate(stringsSeparated):
        
        # Split by word
        words = string.split()
        for k, word in enumerate(words):
            
            # Get the keys from the dataframe as a list (it is initially extracted as a generator type)
            dfList = list(dataframe.keys())
            keys = str(dfList)
            
            # If the first key is a digit, we assume that the keys are from the row below the keys instead
            if keys[0].isdigit():
                keys = dataframe[dfList[0]]

            # Put all of the keys in a single string
            keysAll = ""
            for key in keys:
                keysAll += key

            # Since a row should be horizontal, we check the similarity between that of the text by line.
            similarRating = similar(words, keysAll)
            if similarRating > similarityAmt: # If similarity rating (which is a ratio from 0 to 1) is above the similarity amount, we approve of it
                for j in range(10): # Iterate upwards 10 lines above until we are capable of finding a line that is longer than 4 characters (this is an arbitrary number just to ignore blank lines).
                    try:
                        separatedString = stringsSeparated[i-j-1]
                        if len(separatedString) > 4:
                            return stringsSeparated[i-j-2]+separatedString # Return the top two lines to hopefully have an accurate name
                        else:
                            continue
                    except:
                        continue
    return "Unnamed"

# Retreive the text from the pdf
pages = convert_from_path(pdf_path, 500) # pdf_path would be the path of the PDF which you extracted the table from
pdf_text = ""
# Add all page strings into a single string, so the entire PDF is one single string
for pageNum, imgBlob in enumerate(pages):
    extractedText = pytesseract.image_to_string(imgBlob, lang='eng')
    pdf_text += extractedText + "\n"

# Get the name of the table using the table itself and pdf text
tableName = find_table_name(table.df, pdf_text) # A table you extracted with your code, which you want to find the name of