In the modern digital era, enterprises face the daunting task of processing enormous amounts of digital data contained within a wide range of documents such as – invoices, receipts, purchase orders, legal documents, medical records, insurance claims, and loan applications. They could be in various formats, like images, PDFs, and emails, in different locations. Efficiently processing this vast array of documents becomes a significant challenge as it is time-consuming, error-prone, and costly.
With Document AI solutions, enterprises can automate document classification and data extraction tasks, eliminating the need for manual data entry and reducing the risk of errors. It empowers businesses to streamline document workflows and allocate their time and resources to more crucial tasks.
In this blog, we will dive deep into the different use cases of data annotation for document processing and some of the best practices.
Types of Annotations for Intelligent Document Processing
Named Entity Recognition (NER)
Named Entity Recognition involves identifying and labeling specific entities within a document. For example, if you have the financial report of a company, NER can help identify and label entities like company names, dates, monetary values, or product names. NER enables the AI system to understand and extract critical information from the document.
Named Entity Classification
Named Entity Classification is a step further and categorizes the identified entities into different classes or types. For instance, if you have patient medical history documents, entity classification categorizes the identified entities as medical conditions, medications, procedures, or healthcare providers. It helps in organizing and analyzing the information based on the entity types.
Document Classification involves categorizing entire documents into different classes or categories based on their content, purpose, or topic. For example, you can classify documents as invoices, resumes, contracts, or medical reports. Document Classification helps organize and retrieve documents based on their classification, making handling large volumes of documents a breeze.
Document Transcription refers to converting the content of a document into written or typed text, primarily used for handwritten documents or scanned images of documents.
Document Parsing involves analyzing the structure and layout of a document to extract specific information or elements. For instance, in a legal contract, document parsing identifies and extracts sections like the parties involved, the terms and conditions, or important dates. It helps understand the document structure and extract relevant information in a structured format.
Data/Entity Extraction refers to extracting specific information or data points from a document. For example, if you have an invoice, data extraction can identify and extract information such as the invoice number, due date, total amount, or item details. It helps automate data entry tasks and extract structured information from unstructured documents.
These annotations play crucial roles in document AI by enabling the system to understand, classify, and extract information from documents, making them more actionable.
Data Annotation Best Practices for Document AI
Efficient and effective data annotation requires adherence to best practices to ensure the reliability and quality of annotated data. Let us look at essential best practices for data annotation in Document AI, empowering organizations to enhance accuracy and efficiency in their AI systems.
Define Clear Annotation Guidelines
Establishing clear and comprehensive annotation guidelines is crucial. Clearly define the annotation tasks, including the types of entities, formats, and other specific instructions to ensure consistency among annotators.
Provide Adequate Training to Annotators
Investing in proper training for annotators is essential. Educate them about the annotation guidelines, specific document types, and the expected outcomes. The training ensures the team understands the labeling task, resulting in consistent and accurate annotations.
Quality Assurance and Iterative Feedback Loop
Implement a robust quality assurance process to review the annotated data. Regularly check a sample of annotations to assess the quality, consistency, and adherence to guidelines. Provide feedback to annotators, addressing any issues or concerns to help improve the annotation quality.
Manage Annotation Workload
Ensure a manageable workload for annotators to maintain consistent quality. Overburdening annotators can lead to errors and inconsistencies. Distribute the workload evenly and allow sufficient annotation time.
Leverage Iterative Annotation Approach
For large-scale annotation projects, consider adopting an iterative approach. Start with a smaller annotated dataset and gradually expand it based on the evolving requirements. This approach allows for ongoing improvements and adjustments, ensuring flexibility and adaptability to changing annotation needs. Before launching the entire batch, run a sample batch to clarify instructions, edge cases, and approximate task times.
Collaborate and Communicate
Promote collaboration and communication among annotators, domain experts, and AI practitioners. Encourage open discussions, clarify annotation guidelines, and create a platform for sharing insights and best practices. This collaboration helps maintain a unified understanding of the annotation task and improves the overall quality of the annotations.
Continuous Learning and Feedback Incorporation
Document AI systems evolve, so it is essential to incorporate feedback and lessons learned into the annotation process. Encourage annotators to share their insights and experiences. This feedback loop contributes to refining annotation guidelines, improving efficiency, and ensuring continuous learning and improvement.
Data annotation is a critical component in the success of Document AI systems. By following these best practices, organizations can enhance the accuracy and efficiency of data annotation processes, resulting in improved AI models.
The need for scale in high-quality document annotation has never been greater. iMerit combines the best predictive and automated annotation technology with world-class data annotation and subject matter experts to deliver the data you need to get to production fast.
iMerit’s data experts work with clients to calibrate their quality and throughput requirements and build custom workflows for their project needs. We deliver the highest quality results of cost-effective human labeling across batches and iterations.