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

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'''

请您参考如下方法:

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