The basics of IDP
In our modern world, documents are ubiquitous, manifesting in physical paper prints and various digital formats such as Word documents, PDFs, and images. To process these documents and comprehend their data, we often must interpret their meaning.
With the advent of Optical Character Recognition (OCR) technology, we have trained computers to recognize the shapes on paper and convert them to digital data. However, Intelligent Document Processing (IDP) technology elevates OCR by integrating multiple technologies, such as Natural Language Processing (NLP) and Machine Learning (ML).
Not only can IDP extract data from documents, but it can also analyze and interpret the text. Furthermore, the technology can recognize the document type and sort it into pre-assigned categories based on its content.
By employing IDP, businesses can reduce manual labor by automating data entry and related processes, ultimately streamlining their workflows. In addition, automation can mitigate the risk of security breaches or fraud. In sum, IDP represents a technological advancement that promises significant benefits for businesses seeking to increase efficiency, accuracy, and security in their operations.
Following the video that summarizes the mechanics and applications of IDP, Ian Barkin responded to a question on the same with a poll.
The question is – “What makes an IDP solution good?”
The poll asked his connections which of the following features of IDP makes it suitable –
- It can read many document types (PDFs, Scans, etc.)
- It accurately interprets ALL data, including handwritten notes, stamps, signatures, images, etc.
- It can guarantee outcomes (e.g., 99% accuracy or meeting SLAs).
- It evolves and learns over time with more input and document types.
Most respondents (57%) answered that the best feature of IDP is that it evolves and learns!
What makes for a good IDP?
- Breaking complex documents into simpler components
Intelligent Document Processing (IDP) involves automating document processing tasks using AI and machine learning technologies. One of the critical benefits of IDP is the ability to break down complex documents into simpler components, which can be analyzed and processed more efficiently. This process involves separating different types of information within a document, such as text, tables, images, and other data types. By breaking down complex documents in this way, IDP can help to improve accuracy and efficiency in document processing tasks.
- Leveraging the best AI model(s) to process each
Once a document has been broken down into components, IDP uses AI models to analyze and process each component. These models can be trained using machine learning techniques to recognize different types of information within documents, such as named entities, keywords, or specific data types. Depending on the nature of the information, other AI models may be more effective for processing specific document components. IDP leverages the best available models for each document component to achieve the highest levels of accuracy and efficiency.
- Combining and unifying results into a single output
After processing each document component, IDP combines and unifies the results into a single output. This output can take various forms, depending on the organization’s needs. For example, it could be a structured data format easily imported into a database or used for further analysis. Or it could be a human-readable report summarizing the essential information extracted from the document. Whatever the form, the goal of IDP is to provide a unified output that is accurate, complete, and useful for the intended purpose.
- Offering crowd or in-house humans-in-the-loop
While IDP is designed to automate many document processing tasks, there are situations where human input is necessary to ensure accuracy and quality. For example, there may be cases where the AI models used by IDP are uncertain about how to process certain information in a document. In these cases, IDP can offer a humans-in-the-loop option, where a human expert reviews the results to verify accuracy and provide additional context. This can be done either in-house or by crowdsourcing the task to a group of remote workers.
- Learning and adapting quickly to new documents/formats
Finally, IDP is designed to be flexible and adaptable to new documents and formats. IDP can quickly adapt to these changes as organizations receive new types of documents or as existing documents are updated or modified. This is achieved through ongoing training and optimization of the AI models used by IDP, as well as advanced techniques such as transfer learning, which can allow models to learn from new data more quickly and effectively. As a result, IDP can provide organizations with a powerful tool for managing document processing tasks with greater speed, accuracy, and efficiency.