From Chaos to Clarity: How Document Understanding Organizes Information

Research suggests that workers spend anywhere from 19-25% of their workweek searching for the documents and information they need to do their jobs. This is an understandably frustrating reality for many employees and has been compounded in recent years as companies embrace a growing number of cloud-based applications. Fortunately, continuous improvement in artificial intelligence (AI) is making it possible to outsource information extraction and retrieval to machines. AI-enhanced automated document management refers to the application of AI techniques to the task of managing large collections of documents. This can include:

• Organizing documents
• Searching for specific information 
• Extracting structured data from unstructured documents
• Identifying named entities
• Extracting dates and locations
• Recognizing the core topics of a document

Document understanding is an important part of automated document management. The tools used for automated document understanding interweave complementary techniques to extract information from the language, logical structure, and context. By using natural language processing (NLP), machine learning (ML), and other such tools, document understanding systems can automatically extract meaningful information from unstructured, semistructured, and structured documents at scale. 

This article explores the basics, benefits, challenges, components, and applications of automated document understanding and its place in the larger scheme of automating complex business operations. 

What is document understanding?

The Institute of Electrical and Electronics Engineers (IEEE) defines document understanding as “the logical and semantic analysis of documents to extract human understandable information and codify it into machine-readable form.” This technology has numerous applications, including information retrieval, document classification, and machine translation. 

Initially, document understanding solutions were limited to simple tasks such as optical character recognition (OCR), which involves converting scanned images of text into digital text that can be edited and searched. With the advent of deep learning and other AI techniques, however, document understanding has become much more sophisticated.

Today, document understanding systems use NLP, ML, and other AI techniques to learn from large amounts of training data. By analyzing a large number of documents, AI-based document understanding tools can learn to recognize patterns and make predictions about the content of a new document. Modern document understanding tools not only extract text from documents but also understand the meaning and context of the text, allowing them to perform more complex tasks such as summarizing text, translating it into other languages, and even generating responses to questions based on the information in the document. 

Benefits of automated document understanding

Document understanding systems can provide several benefits, both for individuals and organizations, including:

• Improved efficiency: Document understanding systems can help to automate tasks that would otherwise be done manually, such as extracting information from a large number of documents. This can save time and resources and allow individuals and organizations to focus on intellectually challenging tasks and strategic decision-making that add to the bottom lines and work satisfaction.
• Enhanced accuracy: Document understanding systems can help reduce the risk of errors in tasks such as data entry and information extraction. By relying on AI and NLP technologies, these systems can provide more accurate results than would be possible with manual processes.
• Better decision-making: The availability of accurate and comprehensive information can help individuals and organizations make better decisions. For example, the intelligent extraction and automated analysis of data from financial documents can help a company to make more informed business decisions.
• Transparency of and access to data: Document understanding systems can help to make information more accessible to people across the board.  In particular, it can help people with visual impairments or other disabilities access data as well. For example, an OCR system could be used to convert a scanned document into digital text that can be read aloud by a screen reader.  This enhances inclusivity in the workplace.

Challenges to machine-based document understanding

One of the main challenges to the automation of document understanding is the complexity of human language. Language is characterized by ambiguities and nuances, which makes it difficult for computers to understand the meaning of a text without additional context or information. 

Another challenge is the diversity of written documents. Different types of documents, such as news articles, scientific papers, and legal documents, have their unique features, formats, and structures. Additionally, documents of the same type may still contain differences in format that can make it difficult for certain AI models to comprehend consistently. Document understanding systems must be able to handle a wide range of document types and be able to adapt to the specific characteristics of each type.

Yet another challenge is the large volume of data that must be processed in order to train a document understanding system. ML algorithms require large amounts of training data to learn effectively. For example, some document understanding systems require 25-50 document samples for each new field a user wishes to automate the processing of.

Document understanding in dealing with unstructured data in business operations

Surveys suggest that more than 95% of businesses have some need to process unstructured data.  This includes unstructured documents, which do not have a predefined format or structure, such as free-form text or handwritten notes. Modern document understanding systems can use optical character recognition (OCR) to extract text from scanned images of handwritten or printed documents. This can be useful in a variety of applications, such as digitizing historical documents or transcribing handwritten notes.

Even typewritten or digital documents may vary in file type, format, style, and content. Documents of the same type (e.g. invoices) from different sources could vary in terms of layout structures and the location of logical objects, such as names or dates. Discerning the document’s format and the recognition of the appropriate fields that must be extracted requires protocols for complex object detection and image segmentation that mimic the human ability of visual understanding.

The subjective and objective variations in unstructured documents make it difficult for computers to automatically process and analyze their content. To overcome the challenges mentioned above, document understanding solutions use NLP tools and ML algorithms to extract structured information from unstructured documents. By analyzing the text and context of a document, these systems can learn to identify patterns and make predictions about the content of the document. 

Components of document understanding

AI-enabled document understanding solutions use a combination of various tools:

• Natural language processing: This is the ability of a computer to understand and analyze the meaning of human language. NLP algorithms can be used to identify the main topics of a document, to identify named entities (such as people, places, and organizations), and to extract structured information from text.
• Machine learning: ML algorithms are used to analyze large amounts of training data in order to make predictions about new data. In the context of document understanding, machine learning algorithms can be used to classify documents, identify patterns in text, and extract structured information from documents.
• Optical character recognition: OCR algorithms are used to extract text from scanned images of printed or handwritten documents. This can be useful for digitizing historical documents or transcribing handwritten notes.
• Information extraction tools: This refers to the process of extracting structured information from text. Some of the information extraction tools include:‍
• Named-entity recognition (NER) systems: These systems can identify and classify named entities such as people, organizations, and locations in the text.‍
• Relation extraction systems: These systems can identify and classify the relationships between named entities in text.‍
• Summarization systems: These systems can automatically generate summaries of text by extracting the most important information and condensing it into a shorter form.‍
• Question-answering systems: These systems can answer questions based on the information in a document or collection of documents. 

How does automated document extraction work? 

Automated document extraction entails the following steps:

1. Conversion of data into the digital format: The logical first step in document understanding is the conversion of documents of various formats into a common digital format. Converting document data into a digital format, also known as digitization, involves using technology to convert paper documents, images, or other non-machine readable formats into a digital form that can be stored, edited, and shared electronically. This process typically involves scanning the document to create a digital image and then using optical character recognition (OCR) software to convert the text in the image into editable digital text.
2. Pre-processing: This involves cleaning and preparing the digitized text data for analysis. This may include tasks such as tokenization (breaking the text into individual words), stemming (removing suffixes from words), and removing stop words (common words that do not contribute to the meaning of the text).
3. Feature extraction: In this step, the system extracts relevant features from the text data. This may include identifying named entities, extracting dates and locations, and identifying the main topics of the text.
4. Model training: AI-based document understanding systems necessarily include machine learning algorithms that analyze a large dataset of labeled documents in order to learn the patterns and features that are relevant for document understanding. This step is known as training the model.
5. Inference: Once the model has been trained, it can be used to make predictions about new, unseen documents. This is known as inference, and it involves applying the model to the new documents in order to extract structured information from them.
6. Evaluation: Finally, the performance of the model is evaluated to determine how well it can extract structured information from the documents. This may include metrics such as accuracy, precision, and recall.
7. Data integration: Once the data is meaningfully extracted, it is integrated; data integration is the process of importing and organizing documents from various sources into a single document management system. This can help to make the documents more easily accessible and searchable, allowing users to find and retrieve the information they need more quickly and efficiently.

Applications of document understanding

Document understanding tools can be used in a wide variety of applications in industries such as finance, healthcare, law, and government. Some examples of specific application areas for document understanding include:

• Data entry: Document understanding systems can be used to automatically extract information from documents, such as invoices or purchase orders, and enter it into a database or other system. This can save time and reduce the risk of errors compared to manual data entry processes.
• Financial analysis: Document understanding systems can be used to extract and analyze data from financial documents, such as annual reports or regulatory filings. This can help financial analysts to better understand a company's financial performance and make more informed decisions.
• Healthcare: Document understanding systems can be used to extract information from medical records and other healthcare documents, such as doctors' notes or lab results. This can help healthcare providers to better manage patient records and make more informed treatment decisions.
• Legal: Document understanding systems can be used to extract information from legal documents, such as contracts or court filings. This can help lawyers and other legal professionals to more easily search and analyze large volumes of legal documents.
• Human Resources: The HR department routinely deals with a range of documents that include job applications and resumes to employee contracts and performance evaluations. Document understanding allows HR personnel to make better-informed decisions when it comes to hiring, managing, and developing employees. By being able to quickly and accurately process the information contained in written documents, HR professionals can easily identify the most qualified candidates for open positions and can also more effectively manage the performance and development of existing employees.
• Supply chain management: Document understanding solutions can help in the automation of the supply chain and can help coordinate activities such as tracking orders, managing inventory, and ensuring timely and accurate delivery of goods. 
• Manufacturing: Document understanding can help automate and streamline processes by allowing systems and machines to accurately interpret and extract relevant information from documents typically used in the manufacturing process. This can include things like production orders, work instructions, and quality control documents. By using document understanding, manufacturers can reduce the need for manual data entry and improve the accuracy and efficiency of their operations. This can help reduce errors, improve production times, and ultimately improve the overall quality of their products.
• Government: The public sector routinely deals with documents that may range from legal documents and reports to emails and other forms of written communication. Effective document understanding would enable public sector organizations to make better-informed decisions, as they can quickly and accurately process the information contained in the documents they receive. Additionally, it can reduce the amount of time and effort that is required to manually review and understand large volumes of written information.

What comes naturally to humans is hard for machines

AI tools are designed to mimic the human mind in performing tasks that are time and effort-consuming. Document understanding, an apparently instinctive activity in the literate human, is actually a complex process that involves reading, understanding, and interpreting the text from documents. AI-driven document understanding systems follow the same route and use a combination of computer vision and machine learning abilities to build an intelligent representation of content. As with human understanding, automated document understanding is influenced by factors such as prior knowledge acquired through the learning process and the ability to read and understand the language used in the document. 

Automated document understanding solutions can save considerable time and money for businesses and can play a fundamental role in bringing all company activities under a common digital platform. To get a customized demo of our Intelligent Document Processing (IDP) solution with your documents, book a demo using the form below.