In today’s digital world, businesses are generating vast amounts of data every day. Documents, such as invoices, receipts, purchase orders, and financial statements, are critical pieces of information for any business, but extracting valuable insights from them can be a time-consuming and costly process.
This is where Intelligent Document Processing (IDP) comes in – a technology that automates the extraction and processing of data from documents.
So, what is intelligent document processing?
Intelligent document processing explained
Intelligent Document Processing is a process of automating the extraction and processing of data from structured and unstructured documents. It involves using a combination of Optical Character Recognition (OCR), Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI) algorithms to extract data and automate manual document-based processes.
IDP is different from traditional OCR, which can only recognize text characters and digitize them into a digital format. IDP, on the other hand, uses OCR as a starting point, then applies machine learning and AI techniques to identify patterns and extract relevant data from the document. It then processes this data according to predefined rules and workflows, making it easy for businesses to extract valuable insights and automate processes such as invoice processing, contract management, and financial statement analysis.
How does intelligent document processing work?
Intelligent Document Processing works in three stages: data capture, data extraction, and data processing.
Data capture
The first stage of IDP is data capture. It involves scanning or uploading a document to a system that can extract and process data. The system then uses OCR technology to recognize text characters in the document and convert them into a digital format. OCR is an essential part of IDP because it enables the system to read and understand the document.
Data extraction
The second stage of IDP is data extraction. It involves using machine learning and AI algorithms to extract relevant data from the document. The system applies various techniques such as entity recognition, sentiment analysis, and named entity recognition to identify patterns in the document and extract data such as customer names, product descriptions, and transaction amounts.
Data processing
The final stage of IDP is data processing. It involves processing the extracted data according to predefined rules and workflows.
For example, the system can automatically generate an invoice based on the extracted data, route the invoice for approval, and update the accounting system with the payment information. IDP eliminates the need for manual data entry and processing, making it faster and more efficient.
Benefits of intelligent document processing
IDP offers numerous benefits to businesses. Some of these benefits include:
Improved Efficiency
IDP automates manual document-based processes, reducing the time and effort required to extract and process data. This improves efficiency and enables businesses to focus on more critical tasks.
Increased Accuracy
IDP uses machine learning and AI algorithms to extract data, making it more accurate than manual data entry. This reduces the risk of errors and improves the quality of data.
Cost Savings
IDP eliminates the need for manual data entry and processing, reducing labor costs and improving overall cost savings.
Better Decision-Making
IDP provides businesses with valuable insights from documents, enabling them to make better-informed decisions. For example, businesses can use IDP to analyze customer invoices to identify trends in customer behavior and adjust their marketing strategy accordingly.
Popular use cases of IDP
Intelligent Document Processing has several use cases across various industries. Here are some examples of how IDP can be applied.
Accounting and Finance
IDP can help automate tasks such as invoice processing, purchase order processing, and financial statement analysis. With IDP, businesses can extract data from these documents and process them faster and more accurately, reducing errors and improving overall efficiency.
Healthcare
This form of data processing can also assist in automating patient record processing, medical coding, and insurance claim processing. By automating these tasks, healthcare providers can reduce the time and cost associated with manual data entry and processing, and ultimately improve patient care.
Human Resources
In addition, intelligent data processing can help streamline tasks such as employee onboarding, payroll processing, and contract management. By automating these tasks, businesses can reduce the time and cost associated with manual data entry and processing, and ensure compliance with regulatory requirements.
Legal
IDP can help automate tasks such as contract review and legal document processing. By automating these tasks, law firms can reduce the time and cost associated with manual data entry and processing, and improve overall efficiency.
Manufacturing
IDP can assist in automating tasks such as purchase order processing, shipment processing, and quality control. By automating these tasks, manufacturers can reduce the time and cost associated with manual data entry and processing, and improve overall efficiency.
Retail and E-commerce
This data processing technique can furthermore help automate tasks such as order processing, invoice processing, and inventory management. By automating these tasks, retailers can reduce the time and cost associated with manual data entry and processing, and improve overall efficiency.
Conclusion
Intelligent Document Processing is an essential technology for businesses that generate vast amounts of data from documents. It automates manual document-based processes, improves efficiency, accuracy, and provides valuable insights for better decision-making. IDP is an evolving technology, and as it becomes more advanced, it will continue to transform how businesses operate and compete in the digital age.
