Intelligent Document Processing: The Complete Guide

Intelligent Document Processing (IDP) is the process of automating data extraction and processing by leveraging AI technologies like Machine learning, OCR, and NLP. This whole process involves multiple steps that require constant focus and attention.
Here’s a general overview of how Intelligent Document Processing works:

1. Document ingestion
   The intelligent document processing platform receives unstructured documents in various formats, such as PDFs, scanned images, or electronic files. These documents can include invoices, purchase orders, contracts, forms, or any other type of document containing textual information. By using OCR intelligent document processing tools scan image-based documents into machine-readable text. OCR technology identifies and extracts the characters and words from the document images, enabling the system to work with the textual content.
2. Document Classification
   The system classifies the documents based on their type or purpose. For example, it can differentiate between invoices, receipts, or contracts. This step helps determine the relevant data extraction rules and processing workflows for each document type.
3. Data Extraction
   Using a combination of rule-based approaches and machine learning algorithms, the intelligent data extraction identifies and extracts relevant data fields from the document. The extraction can involve locating and capturing specific information such as customer names, addresses, invoice numbers, due dates, and line items. Machine learning techniques can be employed to improve extraction accuracy over time.
4. Data Validation and Verification
   The extracted data is validated against predefined rules and cross-checked with external databases or systems for accuracy and completeness. For example, the system may verify the extracted invoice amount by comparing it to the corresponding purchase order or receipt.
5. Data Integration and Storage
   The extracted and validated data is then transformed into a structured format and integrated into existing business systems or databases. This allows for further processing, analysis, or archival purposes. The data can be stored in a structured format such as a database or transmitted to downstream applications through APIs or other integration methods.
6. Exception Handling and Human Review
   In cases where the IDP system encounters ambiguous or low-confidence extractions, it can flag them as exceptions. These exceptions are typically routed to human reviewers or subject matter experts who validate and correct the extracted data manually. This feedback loop helps improve the system’s accuracy and learn from its mistakes.
7. Continuous Learning and Improvement
   The IDP system employs machine learning techniques to continuously improve its performance over time. The system can learn from the feedback provided by human reviewers, track extraction accuracy, and adapt its algorithms to handle new document types or variations in document formats.

Intelligent Document Processing (IDP) solutions are designed to automate the processing of various types of documents and data. These solutions use a combination of technologies like Optical Character Recognition (OCR), Natural Language Processing (NLP), and machine learning to extract, understand, and process data from documents. The types of data that can be processed by IDP solutions include, but are not limited to: 

Intelligent Document Processing (IDP) can be applied to various use cases across industries where there is a need to process and extract information from unstructured documents. Here are some common use cases for IDP: 

1. Invoice Processing

   Intelligent document processing tool can automate the extraction of relevant data from invoices, such as vendor information, invoice numbers, line items, and amounts. It can validate the extracted data against purchase orders or contracts, enabling efficient accounts payable processes.

2. Customer Onboarding

   Intelligent document processing tool can automate the extraction of customer information from documents like application forms, identification documents, and financial statements. This speeds up the onboarding process, reduces manual errors, and improves customer experience.

3. Loan Application Processing

   Intelligent document processing platform can extract data from loan applications, including applicant details, financial statements, and supporting documents. This enables faster and more accurate loan application review and decision-making.

4. Claims Processing

   IDP can automate the extraction of data from insurance claims forms, medical records, and supporting documents. This accelerates the claims processing cycle and improves accuracy in determining coverage and payouts.

5. Human Resources (HR) Processes

   Document processing solution can automate HR processes by extracting data from resumes, employee onboarding forms, performance evaluations, and other HR documents. This streamlines HR workflows, improves data accuracy, and enhances employee experience.

6. Healthcare Records Processing

   IDP can also extract relevant information from medical records, lab reports, and patient forms. This assists in accurate medical coding, billing, and streamlines healthcare record management.

When selecting an intelligent document processing (IDP) solution, it’s crucial to consider various factors to ensure it aligns with your organization’s specific needs. Here’s a rephrased version of the paragraph:
Choosing the right IDP solution requires careful consideration of several factors.

1. Begin by identifying your organization’s data processing requirements, such as the format of received or stored data (email, scanned documents, physical paper, etc.), whether the data is structured or unstructured, and the volume and frequency of data that needs to be automated.
2. Once you understand your data processing needs, assess which datasets would benefit most from IDP. Focus on documents that require significant manual processing time, as they are ideal candidates for automation.
3. Next, compare different IDP software options. Here are a few factors to keep in mind while choosing and comparing IDP solutions
   • Consider factors such as the expected accuracy level compared to manual error rates and the potential for improvement.
   • Determine if the IDP technology is template-based or capable of handling complex data formats that lack a prescribed structure.
   • Verify if the software can effectively read and understand the types of data and documents your organization deals with.
   • Assess the ease of integration with your preferred business tools and whether customization is possible.
   • Evaluate the scalability of the software to handle your expected data volume and future growth.
   • Consider the implementation timeline and the level of support provided by the vendor. Lastly, compare quotes from different providers to gain insights into pricing.

AutomationEdge DocEdge is an intelligent document processing solution that comes with AI technologies like machine learning, OCR, and NLP that can automate data extraction across enterprises. By automating multiple data process, businesses can enable employees to get rid of repetitive work and take care of decision making work more. With AutomationEdge’ss DocEdge you get-

• Why is intelligent document processing important?
  Intelligent document processing is important to manage data better for multiple business processes and reduce the manual efforts involved in intelligent data extraction and processing.
• What technology is used for intelligent document processing?
  Intelligent document process solution use multiple AI technologies like OCR, NLP, and machine learning in combination with automation to automate data extraction and processing workflow.
• What is the difference between intelligent document processing and automated document processing?
  Automated Document Processing focuses primarily on automating manual processes, reducing reliance on manual data entry, and improving efficiency. On the other hand, Intelligent Document Processing (IDP) goes beyond basic automation and incorporates advanced technologies such as Natural Language Processing (NLP) and Machine Learning (ML) algorithms to understand and interpret the content of documents, extract relevant information accurately, and perform complex data validation,
• What is the Difference Between IDP and OCR?
  OCR focuses on extracting the textual content from documents, such as printed text, numbers, or handwriting, and does not involve advanced analysis or understanding of the document’s content beyond the text itself.
  On the other hand, IDP is a broader concept that encompasses OCR as one of its components. IDP leverages OCR technology but goes beyond simple text extraction to intelligent data extraction.
• How Does IDP work with RPA?
  Intelligent Document Processing (IDP) and Robotic Process Automation (RPA) are complementary technologies that can work together to automate end-to-end business processes involving document handling and data processing.