Invoice data extraction is a complex task that requires handling both structured and unstructured data, processing different types of PDFs, and understanding where machine learning fits into the picture. Invoices are documents that outline the details of a transaction between a buyer and a seller, including date, names and addresses, product descriptions, quantities, prices, and total amount due. Extracting data from invoices can be expensive and lead to delays in payment processing, especially when dealing with large volumes of invoices. Python offers several powerful tools for invoice extraction, each suited for different aspects of the problem, such as Pytesseract OCR for scanned images, Pandas for tabular data, Tabula for extracting tabular data from PDFs, Camelot for handling complex table structures, OpenCV for image processing, and Pillow for image manipulation. Preparing data before extraction is an important step in the invoice processing pipeline, involving techniques such as data cleaning and preprocessing to identify and correct errors, inconsistencies, and other issues in the data. Extracting data from invoices using Python involves combining different techniques, including regular expressions, OCR, and machine learning, to handle various invoice formats. Automated platforms like Nanonets provide an easy-to-use solution for businesses looking to streamline their invoice data extraction process, offering a range of features, including cloud-hosted models, state-of-the-art algorithms, and field extraction made easy.