The Intelligent Document Processing solutions market size was USD 1.2 billion in 2021 globally and will reach USD 1,452 million in 2022. Finance, insurance, law, healthcare, supply-chain management, or hospitality – no industry is untouched by the powerful effects of automated document data extraction. This transformative technology, powered by artificial intelligence and machine learning, enables contextual information to be extracted from documents, providing valuable insights.
By leveraging intelligent document processing (IDP), businesses can streamline their document processing workflows, reducing the need for extensive human intervention. It, in turn, frees up valuable human resources, allowing them to focus on more critical tasks and critical decision-making.
Unlike template-based and rule-based data capture solutions that can only recognize characters, intelligent document processing (IDP) systems can comprehend and make sense of the captured data. Template-based optical character recognition (OCR) solutions may be able to read documents, but they cannot truly understand the content. Here is where humans can help.
In this blog, we will understand the role of human-in-the-loop in Intelligent Document Processing, its benefits, and how it improves model performance.
What is Human-in-the-Loop (HITL)?
HITL, or Human-in-the-Loop, is a collaborative approach that combines human intelligence and machine automation in document AI workflows. It involves human experts who validate, refine, and enhance the outputs of automated systems. By leveraging human judgment, expertise, and contextual understanding, HITL improves accuracy, addresses complex cases, resolves ambiguities, and continuously improves document analysis and processing.
What does the Document AI Workflow look like?
Dataset Curation + Document Pre-processing
The dataset is curated and prepared for analysis in this initial stage of the document AI workflow. It involves gathering relevant documents, organizing them, and performing pre-processing tasks such as data cleaning, noise reduction, and deskewing. This step ensures the documents are in a suitable format for subsequent stages.
Document Classification
Document classification is a crucial step in the workflow, where documents are categorized based on their content, purpose, or predefined criteria. Machine learning algorithms are applicable in classifying documents into different categories or types automatically. It enables efficient handling and processing of documents in subsequent stages.
Data Extraction
Data extraction focuses on extracting valuable information from documents. It automatically identifies and captures specific data elements such as names, addresses, dates, and other relevant fields. Techniques like optical character recognition (OCR) and natural language processing (NLP) extract structured data from unstructured documents, making it readily available for further analysis and processing.
Data Validation
Data validation plays a critical role in ensuring the accuracy and reliability of the extracted information. Automated validation algorithms compare the extracted data against predefined rules, patterns, or reference databases for potential errors or inconsistencies. Any inconsistencies or discrepancies get flagged for further investigation or correction.
Human Review
Human review is essential to introducing the Human-in-the-Loop (HITL) approach to the document AI workflow. Human experts review and verify the extracted data for accuracy, completeness, and contextual understanding. They apply their domain expertise and judgment to resolve ambiguities, handle edge cases, and address complex scenarios that automated algorithms may struggle with. The human review stage adds an extra layer of validation and ensures the reliability of the extracted data.
By incorporating these stages into the document AI workflow, companies can streamline their document processing, enhance efficiency, and achieve higher accuracy.
Benefits of HITL in Document AI Workflows
Contextual information is crucial for accurate data interpretation, a capability that IDP brings. However, human review is still necessary to validate the extracted data for higher accuracy.
Enhanced Accuracy: HITL in document AI workflows improves accuracy by involving human experts to identify and resolve complex, ambiguous, or rare document cases. Human judgment and expertise complement automated algorithms for more precise and reliable document analysis and processing.
Adaptability to Complex Scenarios: HITL allows for the human interpretation of information from documents with diverse formats, layouts, languages, and edge cases, overcoming the limitations of automated algorithms.
Handling Ambiguous Data: HITL workflows excel in resolving ambiguities and inconsistencies in content, ensuring accurate data extraction and analysis.
Continuous Model Improvement: HITL enables the iterative feedback loop between humans and machines, with human feedback used to train and fine-tune machine learning models for improving document AI accuracy over time.
HITL Success Story: iMerit Improves Quality Model and Saves Employee Time by 80% for CrowdReason
iMerit has had a long and fruitful engagement with CrowdReason, a technology services company that provides property tax software and custom data services. CrowdReason needed large volumes of taxation data to be processed and structured quickly and accurately.
iMerit provided the human intelligence required by answering specific questions about the information within the document, such as source, due date, the amount, and so on, thereby extracting salient data points at scale. Instead of CrowdReason carrying out the workflow, iMerit annotators now entered the data themselves. Three separate iMerit annotation experts evaluated the outputs for continually testing and improving algorithm accuracy. With an automated process, CrowdReason’s employees now spend 80% less time manually entering data.
Read the Case Study
“They continue to work our data exceptions, acting as the “human-in-the-loop.” Whenever we have low confidence in our results, iMerit resolves those data points for accuracy. They provide a secure workforce, which gives us confidence that our client data will remain private.”
– Brandon Van Volkenburgh, CrowdReason CTO
Conclusion
Incorporating Human-in-the-Loop (HITL) in document AI workflows enables the curation and pre-processing of datasets, accurate document classification, precise data extraction, rigorous data validation, and meticulous human review. This combination of automated processes and human involvement brings forth reliable and high-quality results.
Embracing HITL is the key to staying ahead in document AI, enabling businesses to extract maximum value from document analysis efforts and achieve significant competitive advantages.
iMerit’s solution provides domain expertise in data extraction technologies and techniques, guaranteeing SLAs and high-quality data across multiple domains.
