With the shift to digital documents, extracting data from scanned documents through OCR and machine learning has become crucial for convenience. To enable accurate data extraction, research facilities and corporations have advanced computer vision and Natural Language Processing (NLP). Deep learning now allows extracting far beyond just text from scans – tables, key-value pairs, and more can be extracted. Many OCR data extraction solutions provide products to extract data from scanned documents, meeting the needs of individuals and businesses for document data extraction. Data extraction is the process of converting unstructured data into interpretable information by programs, which allows humans to process the data further. The most common task in data extraction from scanned documents is extracting text, while other tasks include extracting tables, key-value pairs, and figures. Computer vision methods are heavily used to achieve high-accuracy table extraction. Key-value pairs (KVPs) are a common alternative format used for data storage in documents, but finding the underlying structures to automatically perform KVP extraction is an ongoing research process. Figures within scanned documents are also important to extract, as statistical indicators such as pie charts and bar charts often include crucial information. Data extraction involves Optical Character Recognition (OCR) and Natural Language Processing (NLP). OCR extraction converts text images into machine-encoded text, while NLP analyzes the words to infer meanings. Deep learning has a major role behind the hype of the artificial intelligence era and has been constantly pushed to the forefront in numerous applications. In traditional engineering, our goal is to design a system/function that generates an output from a given input; deep learning, on the other hand, relies on the inputs and outputs to find the intermediate relationship that can be extended to new unseen data through the so-called neural network. A neural network, or a multi-layer perceptron (MLP), is a machine-learning architecture inspired by how human brains learn. The network contains neurons, which mimic biological neurons and “activate” when given different information. Sets of neurons form layers, and multiple layers are stacked together to create a network to serve the prediction purposes of multiple forms. In computer vision, a type of neural network variation is heavily applied – convolutional neural networks (CNNs). CNNs adopt convolutional kernels that slide through tensors (or high-dimensional vectors) for feature extraction. Many OCR data extraction solutions provide products to extract data from scanned documents, meeting the needs of individuals and businesses for document data extraction. The main goal of data extraction is to convert data from unstructured documents to structured formats, in which a highly accurate retrieval of text, figures, and data structures can be very helpful for numerical and contextual analysis. Business corporations and large organizations deal with thousands of pieces of paperwork with similar formats on a daily basis – Big banks receive numerous identical applications, and research teams have to analyze piles of forms to conduct statistical analysis. Automation of the initial step of extracting data from scanned documents significantly reduces the redundancy of human resources and allows workers to focus on analyzing data and reviewing applications instead of keying in information. Google's document AI allows you to extract a lot of information from documents with high accuracy, offering stunning results in document extraction with their pioneering computer vision technology. Nanonets PDF OCR is completely template and rule independent, making it suitable for various types of PDFs and documents. Deep Reader incorporates multiple state-of-the-art network architectures to perform tasks such as document matching, text retrieval, and denoising images. To implement scanned document data extraction, you can build a simple data-extracting OCR using the Python wrapper for the popular Tesseract OCR engine, PyTesseract, or use Google's Document API, which allows you to extract data from scanned PDFs online with high accuracy.