Intelligent document processing (IDP) is an advanced technology that is used to automatically extract and analyze data from documents. The technology uses various artificial intelligence techniques such as natural language processing (NLP), machine learning, and computer vision to recognize, classify, and extract information from documents.
IDP is a critical technology for businesses that need to process large volumes of documents, such as invoices, receipts, contracts, and purchase orders. The technology eliminates the need for manual data entry, reducing errors and increasing productivity.
In this post, we will discuss the benefits of IDP, how it works, and its potential applications in various industries.
Benefits of Intelligent Document Processing
- Increased Efficiency: One of the primary benefits of IDP is increased efficiency. The technology automates the document processing workflow, eliminating the need for manual data entry. This reduces the time and effort required to process large volumes of documents, allowing businesses to focus on more critical tasks.
- Improved Accuracy: IDP is more accurate than manual data entry. The technology uses machine learning algorithms to extract data from documents, which reduces the chances of errors. This means that businesses can rely on the data extracted by IDP, which can lead to more informed decisions.
- Cost Savings: IDP can save businesses a significant amount of money by reducing the cost of manual labor required to process documents. It also eliminates the need for physical storage of documents, as the technology can store data in a digital format, reducing the cost of physical storage.
- Scalability: IDP can handle large volumes of documents, which makes it a scalable technology. As the volume of documents increases, the technology can adapt to handle the workload, reducing the need for additional resources.
How Intelligent Document Processing Works
IDP uses various artificial intelligence techniques to extract and analyze data from documents. The process involves the following steps:
- Document Input: The first step in IDP is to input the document into the system. This can be done in several ways, such as scanning physical documents, uploading digital documents, or integrating with other systems.
- Pre-Processing: After the document is inputted into the system, the IDP software pre-processes the document. This involves cleaning up the document, removing noise and artifacts, and standardizing the format.
- Optical Character Recognition (OCR): The next step in IDP is OCR, which is used to convert the text on the document into machine-readable text. OCR technology uses algorithms to recognize the characters in the text and convert them into digital text.
- Natural Language Processing (NLP): After OCR, the IDP software uses NLP to extract information from the text. NLP is a branch of AI that focuses on the interaction between computers and human language. NLP can analyze the text on a document to identify key information such as names, addresses, dates, and other relevant data.
- Machine Learning: IDP also uses machine learning algorithms to improve the accuracy of the data extraction. Machine learning algorithms analyze the data extracted from documents to identify patterns and trends. The algorithms can learn from the data, which improves the accuracy of the data extraction over time.
- Data Extraction: The final step in IDP is data extraction. The technology extracts the relevant information from the document and stores it in a structured format. The extracted data can then be used for various purposes, such as reporting, analysis, and decision-making