Financial documents play a critical role in driving business processes, and businesses handle and verify various financial or accounting documents as part of their daily workflows. Organizations have dedicated accounting teams to check financial documents, enter data into accounting software, verify the data against supporting documentation, and process transactions if needed. However, these manual interventions are time-consuming and error-prone, taking up resources that could be put to better use. Optical Character Recognition (OCR) technology can help automate the extraction of financial/accounting data from documents, reducing processing times for each document and improving accuracy. OCR finance or accounting refers to the application of OCR technology to automate the extraction of financial/accounting data from documents, and it can automatically recognize and extract text, characters, fields, or data from scanned documents and images. By leveraging OCR software, businesses can reduce costs, improve processing speed, streamline payment processing, process documents intelligently, improve data accuracy, scale easily, save the environment, and keep employees motivated. OCR finance and accounting have many interesting use cases with respect to AI document processing workflows, including invoice OCR, receipt OCR, banking use cases, insurance companies using OCR for claims processing, simplifying accounting processes related to QuickBooks, and businesses using accounting OCR for improving audits and reports on expenses. Nanonets is an AI-based OCR software that offers convenient pre-trained models for popular financial OCR and accounting OCR use cases, providing better accuracy, AI/ML capabilities, and unique benefits such as handling unstructured data and multi-page documents with ease.