What is intelligent document processing?
Much of the relevant information that commercial organizations can turn to in order to ensure favorable business outcomes exists as unstructured data, in forms such as email, PDF files, typed and handwritten text, and scanned documents.
In its existing form, this data is unstructured as its true content and value cannot be unlocked using conventional digital technologies that must extract data in a structured format, for further analysis.
For years, organizations have been relying on error prone manual processes to extract information from unstructured documents and sources containing semi structured data. Such processes typically require a lot of time, and are prone to human error.
However, digital transformation has enabled the development of sophisticated systems that use artificial intelligence (AI), machine learning (ML) and other technologies to significantly reduce or even eliminate the amount of manual processing (and the associated human error factor) required to perform repetitive tasks and deal with complex documents.
Intelligent document processing (IDP) is used to extract actionable data and insights from unstructured data, semi structured documents, and information streams such as social media posts. Intelligent document processing is performed using natural language processing (NLP), machine learning (ML), and rule based algorithms.
Using robotic process automation (RPA) and tools powered by artificial intelligence, an intelligent document processing solution can unlock the value of unstructured data by transforming it into high quality, structured information for further analysis.
Intelligent document processing software and solutions rely heavily on natural language processing. This is a branch of artificial intelligence that enables computers to understand natural language – the words and sentences that humans use to communicate – and use it to create value.
Within the sphere of natural language processing and intelligent document processing are a number of techniques that include the following.
Information extraction is an automated data processing technique that permits the retrieval of specific information relating to a selected topic, from one or more bodies of text. With IE, it is possible to extract information from unstructured data, semi-structured data, or structured documents with machine-readable text. In natural language processing, information extraction is primarily employed in extracting structured data from unstructured data.
Example: Question answering performed with Huggingface transformers library.
Modern intelligent document processing techniques that fall within the remit of information extraction include automatic annotation, content recognition, and data extraction from images and video.
One of the most prominent techniques used in NLP is question answering. It enables us to ask a question to the context text, and the ML model finds the most appropriate answer from the context text, if it exists.
Sentiment analysis or opinion mining is an NLP technique which scans relevant data in order to establish whether it is positive, negative, or neutral. Among the business functions that rely on sentiment analysis is the monitoring of consumer opinions of products and services, or levels of satisfaction with an organization’s performance and customer support.
With intelligent data capture and sentiment analysis, organizations can also keep track of how they are perceived on social media.
For more accurate ratings of customer satisfaction and consumer opinion, the positive, negative, or neutral poles may be expanded to include very positive and very negative. This forms the basis of five-star classification systems.
Other nuances that sentiment analysis may be set up to detect include feelings and emotions, levels of urgency, and levels of intent (such as interested in buying, or not interested). In all cases, the technique relies on sophisticated machine learning algorithms, and intelligent automation that converts unstructured data into a more structured form suitable for further processing and analysis.
Named Entity Recognition
In document processing analysis, entities are the main components of a sentence, including noun phrases and verb phrases. Named entity recognition is a natural language processing technique that can automatically scan a body of text, identify fundamental entities, and classify them into predefined categories.
Also known as entity identification, entity extraction, or entity chunking, named entity recognition can process large volumes of text to pick out entities such as personal names, location names, company names, monetary values, dates, times, etc.
Named entity recognition can help businesses organize data more efficiently. In recruitment for example, using named entity recognition enables human resources personnel to instantly extract the most relevant information about candidates. The technique is also used in classifying content for news channels, and creating recommendation systems for services like YouTube.
Example: text source - Wikipedia, visualization - SpaCy
As a more efficient and effective alternative to manual data entry and processing, text classification uses NLP to automatically analyze text, and then assign a set of predefined tags or categories to it, based on its content.
Also known as text tagging or text categorization, text classification is one of the foundation stones for sentiment analysis. It also plays a part in topic detection (identifying the theme or topic of a piece of text) and language detection (establishing the language of a given piece of text).
Text similarity is a natural language processing technique that establishes how close or similar two pieces of text are in both word construction (lexical similarity) and meaning (semantic similarity).
For intelligent document analysis, text similarity has value in legal matters. For example, if a a new contract is similar to an existing one that has proven to be resilient, this reduces risk. In the context of customer service, IDP solutions using text similarity can enable support staff to give a uniform response to similarly worded queries or requests (e.g., “Where is my shipment?”, “What happened to my delivery?”).
Relationship extraction is the task of extracting semantic relationships from a body of text. The extracted data typically has relationships between two or more entities of a certain type (e.g. person, organization, location) that fall into a number of semantic categories (such as married to, employed by, lives in). It’s an extension of the named entity recognition and is used when just listing the found entities is not enough, and you need to know the relationship between them.
Let’s say you would like to extract the relation between a person and an organization. In such a case you could have relationships like: founder, CEO, employee, etc.
The NLP system can extract these relationships based on the context of a sentence and output the entities pairs (in this case organization - person) and their relationship (in this case for example “founder” or “CEO”).
In NLP, text summarization is the process of condensing the information in a large body of text into a smaller form, for quicker consumption.
Example: Performed with Huggingface transformers library
Extractive summarization methods identify the most significant sentences of the text, and add them together to form the summary. Abstractive summarization identifies the most important sections, interprets their context, and reproduces them in a new way.
How is IDP different from OCR?
OCR or optical character recognition is a long-established data conversion technique in which an image of text is transformed into a machine-readable form. It is the basis of document scanning. However, optical character recognition typically cannot extract any context from the content, rendering automated data extraction and interpretation impossible.
With advances in automating document processing, optical character recognition is now effectively a sub-process of intelligent document processing. Once OCR converts an image of text into a machine-readable form, advanced AI technologies, including machine learning and deep learning enable IDP to recognize and capture the content from unstructured, semi-structured, and structured data sources, and extract the context, making essential data and insights available for business processes.
What benefits does intelligent document processing offer?
Intelligent document processing offers several benefits, including:
- Document processing automation extends from end to end, starting with data extraction and finishing with the furnishing of insights to the appropriate business process or employee.
- Manual data entry and error prone manual processes are eliminated, freeing workers for more high value activities.
- Advanced document processing technology minimizes errors, increases efficiency, and lowers overall processing costs.
- Intelligent document processing provides scalable solutions - in periods of high data velocity we can just employ more computational power.
- For banks and other institutions, IDP increases efficiency and reduces risk when dealing with financial documents and transaction records.
- The automated workflow speeds data collection and data validation for business documents, and provides a digital audit trail that assists organizations in maintaining security and regulatory compliance.
Don’t waste time and money on manual document processing
Intelligent document processing frees organizations from having to rely on time- consuming and error prone manual processes. It provides robust models dedicated to extract information from unstructured documents and sources containing semi structured data.
NLP, machine learning and tools powered by artificial intelligence allow intelligent document processing solutions to unlock the value of unstructured data by transforming it into high quality, structured information for further analysis. In this way, IDP provides scalable solutions that reduce risk, errors, and processing costs for organizations, while improving their efficiency, security, and compliance status.