Intelligent Document Processing - Workflow Examples and Tips | BP3 Global, Inc.

Introduction

Introduction

In today’s world, the ability to efficiently process and extract information from documents is paramount for organizations. Intelligent Document Processing (IDP) integrates advanced AI technologies to streamline this task.

What is Intelligent Document Processing (IDP)?

Intelligent Document Processing, often referred to as IDP, encompasses software solutions that combine Artificial Intelligence (AI) technologies such as Optical Character Recognition (OCR), Named Entity Recognition (NER), and Machine Learning (ML) to efficiently process various types of documents. The goal is to extract relevant information and subsequently feed it into downstream applications for further use.

Current Market Solutions

There are numerous IDP software products available on the market, each exhibiting similar capabilities. Some of the notable offerings include:

• ABBYY FlexiCapture
• IBM DataCap
• Automation Anywhere IQ Bot
• UiPath Document Understanding

While the functionality may be consistent across these products, their ease of use can significantly differ. Hence, the appropriate choice often depends on the type of data being extracted and the specific requirements for accuracy.

Types of Document Structures

Understanding the structure of documents is crucial for successful data extraction. There are three primary types of document structures:

Structured Documents

Structured documents have a predictable layout where data is located at absolute or relative positions based on known anchor points. Common examples include government forms, tax documents, licenses, and passports. To extract data from structured documents, a Positional Data Extractor is typically used, configured to identify defined anchor points on the page.

Semi-Structured Documents

Semi-structured documents, such as reports, have a standard data order but can vary in length and may cross page boundaries. For instance, patient medical records often illustrate this category. For extracting data from such documents, a Form or Key-Value Data Extractor is more suitable, utilizing machine learning to comprehend the relationships between various keys and values.

Unstructured Documents

Unstructured documents lack a predefined layout, relying instead on the language and grammatical structure. An example includes free-text sections of medical records. Data extraction for unstructured documents employs a Named Entity Recognizer (NER), trained to identify specific terms or entities based on a vast text corpus.

Data Extraction Process Workflow

The IDP extraction process generally follows these steps:

1. Document Ingestion: The document reaches a storage location, initiating the processing workflow.
2. Optical Character Recognition (OCR): OCR engines recognize characters within the document, grouping them into blocks while retaining their locations.
3. Data Extraction: The identified blocks are passed to configured data extractors specialized for the document type.
4. Aggregation and Validation: Multiple instances of the same data might be identified; therefore, an aggregator helps determine the best instance based on confidence levels.
5. Human Validation: For instances where confidence levels fall below thresholds, human intervention may be needed to validate and correct the data.
6. Storage and Automation: Finally, the extracted structured data is stored for future automation and downstream applications.

Leveraging AWS for IDP Solutions

At BP3 Global, we favor leveraging Amazon Web Services (AWS) for IDP solutions due to its robust scalability. Our consultants tailor AWS-based document processing pipelines to meet specific automation requirements, facilitating the processing of hundreds of thousands of documents daily.

Conclusion

In summary, Intelligent Document Processing merges various AI technologies to efficiently handle document extraction. Understanding the different document structures and utilizing appropriate extraction strategies is key for organizations looking to streamline their document processing workflows.

Keywords

• Intelligent Document Processing
• IDP
• AI Technologies
• Optical Character Recognition
• Named Entity Recognition
• Machine Learning
• Document Structures
• Structured Documents
• Semi-Structured Documents
• Unstructured Documents

FAQ

Q1: What is Intelligent Document Processing (IDP)?
A1: Intelligent Document Processing (IDP) refers to software solutions that use AI technologies to automate the extraction and processing of information from various document types.

Q2: What are the common types of document structures in IDP?
A2: The common document structures include structured documents (with a predefined format), semi-structured documents (with a standard order but varying lengths), and unstructured documents (lacking any specific format).

Q3: Which tools are typically used in IDP?
A3: Common tools include ABBYY FlexiCapture, IBM DataCap, Automation Anywhere IQ Bot, and UiPath Document Understanding.

Q4: How does the data extraction process in IDP work?
A4: The process involves document ingestion, OCR processing, data extraction via specific tools, aggregation, human validation if needed, and finally, storage for downstream applications.

Q5: Why is AWS a preferred platform for IDP solutions?
A5: AWS offers scalability and flexibility, allowing BP3 Global to configure and deploy robust IDP solutions tailored to customer needs efficiently.