![]() Manually extracting and processing large volumes of data from documents consumes a lot of valuable time. Benefits of Intelligent OCR and RPA IntegrationĪdding OCR software into your RPA implementation along with NLP techniques provides several benefits to the enterprise. Natural Language Processing (NLP) techniques can be used to make ML models more accurate when converting unstructured information into structured data. Machine Learning models can be trained to translate extracted texts into actionable data. Automated extraction of data using OCR from these types of documents would not be of much use in RPA unless they are converted to structured data. These are documents that do not have a fixed format at all, fields are unpaired and are positioned without consistency. To overcome this, both rule-based and pre-trained ML models can be used to ensure higher accuracy. ![]() These documents may have some amount of consistency such as all copies may have the same fields, but their positioning may vary because of which rule-based data capture may not be accurate. Using OCR and rule-based RPA bots to automate the extraction of data from these documents is relatively easy. For instance, standard forms are likely to have the fields and corresponding values positioned in the documents consistently regardless of the number of copies. This refers to documents that are usually based on a fixed template and the information is in a consistent format. It's important to know the different types of documents to optimize your processes. OCR can digitize and understand any type of document. RPA Integration with OCR Improves Document Processing Efficiency OCR technology can be particularly useful when integrated with Robotic Process Automation (RPA) since the latter is being implemented widely in the field of document processing. Optical Character Recognition (OCR) technology can intelligently capture data from paper documents, images, emails, and more to streamline and automate processes. If there is a high volume of documents, manual efforts become completely unfeasible. Growing industries such as financial services and healthcare still rely on non-digital documents which hinder their digital transformation initiatives.ĭigitizing these documents manually is time-consuming, expensive, and prone to errors as employees tend to slip up when performing mundane tasks. Additionally, many of these documents may be physical paper copies, especially in traditional environments. These documents may contain structured or unstructured data. Enterprises work with tons of different types of documents from invoices to resumes, and beyond.
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