Pattern recognition either by algorithm or with a trained CNN1D model can be used. Using deep learning algorithms-most popularly used are libraries of spacy and transformer. Other convenient hack for readable PDF is to get the table columns, header and footer and deciphering it on the basis of it coordinate with respect to the column area import camelot import pyPdf2 as pyPdf from tabula import read_pdf from matplotlib.pyplot import plt tables = camelot.read_pdf('invoice.pdf' ,flavor='stream') ot(tables, kind='text') plt.show() ot(tables, kind='grid') plt.show() reader = pyPdf.PdfFileReader(open("C:\Users\riley\Desktop\Bank Statements\50340.pdf", mode='rb' )) n = reader.getNumPages() df = for page in : if page = "1": df.append(read_pdf(r"C:\Users\riley\Desktop\Bank Statements\50340.pdf", area=(530,12.75,790.5,561), pages=page)) else: df.append(read_pdf(r"C:\Users\riley\Desktop\Bank Statements\50340.pdf", pages=page))ΔΆ.Structured PDF
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