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Intelligent document processing includes four phases to capture data from unstructured and semi-structured documents: pre-processing, classification, extraction, and post-processing.

IDP is used across industries. Here are some intelligent document processing use cases: Finance departments can automate purchase ledger functions such as processing invoices. Human resources departments can process resumes, screen employees, and process employee surveys and other HR data with IDP.

Infrrd's classification features are based on a combination of AI technologies, such as deep learning and NLP, and proprietary machine learning algorithms. We call it Intelligent Classification. Using Infrrd's IDP, you can create your own classification models and map each document type to specific extraction models.

Implementing intelligent document software begins with an assessment of your document processing workflows to determine which ones can be automated. Once you know the documents you want to automate, you should identify the workflows that require the highest level of accuracy and begin your automation with them.

Intelligent document processing (IDP) applies artificial intelligence (AI) and machine learning techniques to process structured, semi-structured, and unstructured documents that enable technology to read and process content in documents like a human.

It starts with intelligent document recognition, when IDP uses complex deep-learning AI technology to scan documents and classify them. This technology can be trained in up to 190 languages and is capable of reading and interpreting documents much like a data processing worker would.

Intelligent document processing (IDP) is a workflow technology that automates the extraction and classification of unstructured and structured document data by using AI technologies like computer vision, machine learning, and natural language processing.